Patent Publication Number: US-2022237783-A1

Title: Slide-free histological imaging method and system

Description:
RELATED APPLICATIONS 
     The current application claims priority from U.S. Provisional Patent Application No. 62/973,101 filed Sep. 19, 2019, the contents of which is herein incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present invention relates to a slide-free histological imaging method and system. 
     BACKGROUND 
     Histological examination remains the gold standard for surgical margin assessment of malignant tumor. However, routine histological analysis, which involves a lengthy and costly procedure for sample preparation, generates toxic reagent wastes, exhausts small specimens, and prolongs the generation of histopathological reports ranging from hours to days. This lengthy and costly procedure includes formalin-fixed and paraffin-embedding (FFPE), followed by high-quality sectioning, staining, and subsequently mounting of the specimens on glass slides. These unavoidable steps require several days to accomplish, causing a delay in generating accurate diagnostic reports ranging from hours to days. Although intraoperative frozen sectioning offers a faster alternative to FFPE histology by freezing fresh tissue prior to physical sectioning, intraoperative frozen sectioning still takes 20 to 30 minutes for preparation and turnaround. Moreover, frozen sectioned specimens suffer from inherent freezing artifacts especially when dealing with lipid-rich tissues, leading to intraoperative misinterpretations and diagnostic pitfalls. 
     The great demand in histopathology has inspired many efforts in achieving a rapid and non-invasive diagnosis of unstained fresh tissue. Certain microscopy techniques for imaging non-sectioned tissue, including microscopy with ultraviolet (UV) surface excitation, confocal laser scanning microscopy, and light-sheet microscopy, reduce the laborious tasks and treatment costs involved in the preparation of hundreds of glass slides in conventional FFPE histology. However, these methods all require specific fluorescence labeling to improve molecular specificity. Fluorescence imaging, whilst undoubtedly powerful for providing information on morphology and dynamics of different biomolecules in cells, can lead to the use of exogenous labels or gene transfection which interfere with cell metabolism and adversely affect subsequent clinical implementations. Moreover, long term monitoring of cells with fluorescence labelling can cause photo-toxicity to cells and photo-bleaching of fluorophores themselves. 
     Stimulated Raman scattering (SRS) and coherent anti-Stokes Raman scattering (CARS), in which image structures are characterized by intrinsic molecular vibration of a specific chemical bond, offer a label-free alternative for the examination of C-H stretches in lipid-rich structures. Moreover, non-linear processes originated from a non-centrosymmetric interface, including second-harmonic generation (SHG), third-harmonic generation (THG), and their combined modalities, which have demonstrated significant potential for intrinsic characterization of collagen and microtubule structures. However, these methods all require a high-power ultrafast laser to maintain detection sensitivity and molecular contrast, which may not be readily available in most settings. Spectral confocal reflectance microscopy allows label-free high-resolution in vivo imaging of myelinated axons, but still requires a confocal microscope with tunable wavelength capabilities due to the low molecular specificity. Quantitative phase imaging techniques also offer great possibilities for fast refractive-index mapping through the measurement of phase variations in unstained specimens. However, they are mostly integrated into transmission systems and strictly limited by the sample thickness. In addition, reflectance-based imaging techniques such as optical coherence tomography have been translated into intraoperative diagnosis tools for label-free imaging of human breast tissue, however, it is not designed to achieve subcellular resolution and not suitable for probing molecular targets as desired in standard-of-care clinical pathology. 
     SUMMARY 
     It is an object of the present invention to address one or more disadvantages described above or herein, or at least provide a useful alternative. 
     In a first aspect there is provided a computer-implemented method of generating a pseudo-hematoxylin and eosin (H&amp;E) stained image, wherein the method includes: receiving an input image, the input image being an ultraviolet-based autofluorescence microscopy (UV-AutoM) image or an ultraviolet-based photoacoustic microscopy (UV-PAM) image of an unlabeled specimen, wherein the input image is a grayscale image; transforming the input image, using the generative adversarial network, to a pseudo-H&amp;E stained image of the input image; and outputting the pseudo-H&amp;E stained image. 
     In certain implementations, the generative adversarial network is a generative adversarial network with cycle consistency. 
     In certain implementations, the method includes training the generative adversarial network using unpaired input and H&amp;E stained images. 
     In certain implementations, the generative adversarial network comprises of four deep convolutional neural networks including: a first generator deep convolutional neural network configured to transform the input image to a generated H&amp;E image; a second generator deep convolutional neural network configured to transform a H&amp;E image to a generated UV-AutoM or UV-PAM image; a first discriminator deep convolutional neural network configured to discriminate between a H&amp;E image of a training set and a generated H&amp;E image generated by the first generator deep convolutional neural network; and a second discriminator deep convolutional neural network configured to discriminate between a UV-AutoM or UV-PAM image of the training set and a generated UV-AutoM or UV-PAM image generated by the second generator deep convolutional neural network. 
     In certain implementations, the first and second generator deep convolutional neural networks are ResNet-based or U-Net-based generator networks. 
     In certain implementations, the first and second discriminator deep convolutional neural networks are PatchGAN discriminator networks. 
     In certain implementations, the input image received in the form of the UV-PAM image is generated by: controlling a galvo-mirror scanner of a focusing assembly to focus ultraviolet light on a specimen according to a scanning trajectory; receiving, by at least one transducer, photoacoustic waves emitted by the specimen in response to the ultraviolet light; and generating, based on the photoacoustic waves, the UV-PAM image. 
     In certain implementations, the input image received in the form of a UV-AutoM image is an estimated UV-AutoM image generated from a sequence of speckle illuminated images captured according to a scanning trajectory, wherein the estimated UV-AutoM image has a higher resolution compared to each speckle illuminated image of the sequence. 
     In certain implementations, the estimated UV-AutoM image is generated by: a) initializing a high resolution image object based on interpolating an average of the sequence of speckle illuminated images; b) for each speckle illuminated image of the sequence: i) generate the estimated speckle illuminated image by computationally shifting the high resolution image object to a specific position in the scanning trajectory; ii) determine a filtered object-pattern compound in the frequency domain based on the estimated speckle illuminated image in the frequency domain and optical transfer function; iii) determine an updated estimated speckle illuminated image in the frequency domain based on the estimated speckle illuminated image in the frequency domain, the respective captured speckle illuminated image in the frequency domain, the filtered object pattern compound in the frequency domain, and the optical transfer function; iv) updating the high resolution object based on the updated estimated speckle illuminated image, the estimated speckle illuminated image in the spatial domain, and the speckle pattern; v) updating the speckle pattern based on the updated estimated speckle illuminated image, the estimated speckle illuminated image, and the high resolution image object; vi) applying Nesterov momentum acceleration to the high resolution image object and the speckle pattern; and c) iteratively performing step b) until convergence of reconstructing the high resolution image object is detected, the high resolution image object being the estimated UV-AutoM image with enhanced subcellular resolution across centimeter-scale imaging area. 
     In a second aspect there is provided a computer system configured to generate a pseudo-hematoxylin and eosin (H&amp;E) stained image, wherein the computer system includes one or more memories having stored therein executable instructions, and one or more processors, wherein execution of the executable instructions by the processor cause the processor to: receive an input image, the input image being an ultraviolet-based autofluorescence microscopy (UV-AutoM) image or an ultraviolet-based photoacoustic microscopy (UV-PAM) image of an unlabeled specimen, wherein the input image is a grayscale image; transform the input image, using the generative adversarial network, to a pseudo-H&amp;E stained image of the input image; and output the pseudo-H&amp;E stained image. 
     In certain implementations, the generative adversarial network is a generative adversarial network with cycle consistency. 
     In certain implementations, the one or more processors are configured to train the generative adversarial network using unpaired input grayscale image and H&amp;E stained images. 
     In certain implementations, the generative adversarial network comprises of four deep convolutional neural networks including: a first generator deep convolutional neural network configured to transform the input image to a generated H&amp;E image; a second generator deep convolutional neural network configured to transform a H&amp;E image to a generated UV-AutoM or UV-PAM image; a first discriminator deep convolutional neural network configured to discriminate between a H&amp;E image of a training set and a generated H&amp;E image generated by the first generator deep convolutional neural network; and a second discriminator deep convolutional neural network configured to discriminate between a UV-AutoM or UV-PAM image of the training set and a generated UV-AutoM or UV-PAM image generated by the second generator deep convolutional neural network. 
     In certain implementations, the first and second generator deep convolutional neural networks are ResNet-based or U-Net-based generator networks. 
     In certain implementations, the first and second discriminator deep convolutional neural networks are PatchGAN discriminator networks. 
     In certain implementations, the input image received in the form of the UV-PAM image is generated by: controlling a galvo-mirror scanner of a focusing assembly to focus ultraviolet light on a specimen according to a scanning trajectory; receiving, by at least one transducer, photoacoustic waves emitted by the specimen in response to the ultraviolet light; and generating, based on the photoacoustic waves, the UV-PAM image. 
     In certain implementations, the input image received in the form of an estimated UV-AutoM image generated from a sequence of speckle illuminated images captured according to a scanning trajectory, wherein the estimated UV-AutoM image has a higher resolution compared to each speckle illuminated image of the sequence. 
     In certain implementations, the estimated UV-AutoM image is generated by: a) initializing a high resolution image object based on interpolating an average of the sequence of speckle illuminated images; b) for each speckle illuminated image of the sequence: i) generate the estimated speckle illuminated image by computationally shifting the high resolution image to a specific position in the scanning trajectory; ii) determine a filtered object-pattern compound in the frequency domain based on the estimated speckle illuminated image in the frequency domain, and an optical transfer function; iii) determine an updated estimated speckle illuminated image in the frequency domain based on the estimated speckle illuminated image in the frequency domain, the respective captured speckle illuminated image in the frequency domain, the filtered object pattern compound in the frequency domain, and the optical transfer function; iv) updating the high resolution object based on the updated estimated speckle illuminated image, the estimated speckle illuminated image in the spatial domain, and the speckle pattern; v) updating the speckle pattern based on the updated estimated speckle illuminated image, the estimated speckle illuminated image, and the high resolution image object; vi) applying Nesterov momentum acceleration to the high resolution image object and the speckle pattern; and c) iteratively performing step b) until convergence of reconstructing the high resolution image object is detected, the high resolution image object being the estimated UV-AutoM image with enhanced subcellular resolution across centimeter-scale imaging area. 
     In a third aspect there is provided one or more non-transitory computer readable mediums including executable instructions which configure a computer system to generate a pseudo-hematoxylin and eosin (H&amp;E) stained image, wherein the computer system has one or more processor, wherein execution of the executable instructions by the one or more processors configure the computer system to: receive an input image, the input image being an ultraviolet-based autofluorescence microscopy (UV-AutoM) image or an ultraviolet-based photoacoustic microscopy (UV-PAM) image of an unlabeled specimen, wherein the input image is a grayscale image; transform the input image, using the generative adversarial network, to a pseudo-H&amp;E stained image of the input image; and output the pseudo-H&amp;E stained image. 
     In certain implementations, the generative adversarial network is a generative adversarial network with cycle consistency. 
     In certain implementations, the execution of the executable instructions by the one or more processors configure the computer system to train the generative adversarial network using unpaired input grayscale image and H&amp;E stained images. 
     In certain implementations, the generative adversarial network comprises of four deep convolutional neural networks including: a first generator deep convolutional neural network configured to transform the input image to a generated H&amp;E image; a second generator deep convolutional neural network configured to transform a H&amp;E image to a generated UV-AutoM or UV-PAM image; a first discriminator deep convolutional neural network configured to discriminate between a H&amp;E image of a training set and a generated H&amp;E image generated by the first generator deep convolutional neural network; and a second discriminator deep convolutional neural network configured to discriminate between a UV-AutoM or UV-PAM image of the training set and a generated UV-AutoM or UV-PAM image generated by the second generator deep convolutional neural network. 
     In certain implementations, the first and second generator deep convolutional neural networks are ResNet-based or U-Net-based generator networks. 
     In certain implementations, the first and second discriminator deep convolutional neural networks are PatchGAN discriminator networks. 
     In certain implementations, the input image received in the form of the UV-PAM image is generated by: controlling a galvo-mirror scanner of a focusing assembly to focus ultraviolet light on a specimen according to a scanning trajectory; receiving, by at least one transducer, photoacoustic waves emitted by the specimen in response to the ultraviolet light; and generating, based on the photoacoustic waves, the UV-PAM image. 
     In certain implementations, the input image received in the form of an estimated UV-AutoM image generated from a sequence of speckle illuminated images captured according to a scanning trajectory, wherein the estimated UV-AutoM image has a higher resolution compared to each speckle illuminated image of the sequence. 
     In certain implementations, the UV-AutoM image is generated by the one or more processors by: a) initializing a high resolution image object based on interpolating an average of the sequence of speckle illuminated images; b) for each speckle illuminated image of the sequence: i) generate the estimated speckle illuminated image by computationally shifting the high resolution image to a specific position in the scanning trajectory; ii) determine a filtered object-pattern compound in the frequency domain based on the estimated speckle illuminated image in the frequency domain, and an optical transfer function; iii) determine an updated estimated speckle illuminated image in the frequency domain based on the estimated speckle illuminated image in the frequency domain, the respective captured speckle illuminated image in the frequency domain, the filtered object pattern compound in the frequency domain, and the optical transfer function; iv) updating the high resolution object based on the updated estimated speckle illuminated image, the estimated speckle illuminated image in the spatial domain, and the speckle pattern; v) updating the speckle pattern based on the updated estimated speckle illuminated image, the estimated speckle illuminated image, and the high resolution image object; and vi) applying Nesterov momentum acceleration to the high resolution image object and the speckle pattern; and c) iteratively performing step b) until convergence of reconstructing the high resolution image object is detected, the high resolution image object being the estimated UV-AutoM image with enhanced subcellular resolution across centimeter-scale imaging area. 
     Other aspects and embodiments will be appreciated throughout the description of the embodiments. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       Preferred embodiments of the present invention will now be described, by way of examples only, with reference to the accompanying drawings. 
         FIGS. 1A and 1B  are schematics of an example of a general-purpose computer system upon which various arrangements described herein are implemented. 
         FIGS. 2A and 2B  are schematics of an example of an embedded system upon which various arrangements described herein are implemented. 
         FIG. 3  is a schematic of an example UV-PAM system. 
         FIG. 4A  is an example of a UV-PAM image of a gold nanoparticle with 200-nm diameter, wherein the profile along the white dashed line is extracted for averaging. 
         FIG. 4B  is an example of an averaged line profile of four gold nanoparticles, wherein the FWHM (full width at half maximum) of the Gaussian fitting (solid line) is about 613 nm, representing the lateral resolution of the UV-PAM system of  FIG. 3 . 
         FIG. 4C  is an example of an A-line signal of the central position of the gold nanoparticle in  FIG. 4A , wherein the FWHM of the envelope of the A-line signal is 38 ns, which corresponds to 58 μm, representing the axial resolution of the UV-PAM system of  FIG. 3 . 
         FIG. 5A  is a schematic of an example UV-AutoM system. 
         FIGS. 5B and 5C  is a flowchart representing an example computer-implemented method of reconstructing a UV-AutoM image through a sequence of speckle-illuminated low-resolution images. 
         FIG. 5D  is an example of the pseudocode representing the computer-implemented method of reconstructing a UV-AutoM image through a sequence of speckle-illuminated low-resolution images. 
         FIG. 6  shows a graphical representation of an example of an SI reconstruction method and system for reconstructing a UV-AutoM image. 
         FIG. 7A  is an example of a low-resolution UV-AutoM image captured by a 4λ/0.1 NA objective. 
         FIG. 7B  is an example of a high-resolution UV-AutoM image reconstructed by the method of  FIGS. 5B and 5C . 
         FIG. 7C  is a line profile of the lines  700  and  710  marked in  FIGS. 7A and 7B , respectively. 
         FIGS. 8A and 8B  show examples of UV-AutoM images of two leaf samples with rough surface captured by a 4×/0.1 NA objective. 
         FIGS. 8C and 8D  show examples of high-resolution UV-AutoM images reconstructed from a plurality of speckle-illuminated low-resolution images using the method of  FIGS. 5B and 5C . 
         FIGS. 8E and 8F  show a high-resolution reference image of the two leaf samples of captured in  FIGS. 8A and 8B  using a 10λ/0.3NA objective. 
         FIG. 9A  is a UV-AutoM image of the whole mouse brain (FFPE section after deparaffinization, 4-μm thickness) with a scale bar of 500 μm. 
         FIGS. 9B and 9C  are magnified views of boxes  910  and  920  in  FIG. 9A , each with a scale bar of 50 μm. 
         FIGS. 9D and 9E  are bright-field H&amp;E-stained images corresponding to  FIGS. 9B and 9C , each with a scale bar of 50 μm. 
         FIG. 10A  is an example image of a top view of a mouse brain. 
         FIGS. 10B and 10C  are examples of reconstructed UV-AutoM images each depicting a cross-sectional cut through Lines A-A and B-B of the mouse brain of  FIG. 10A . 
         FIGS. 10D to 10H  show high-throughput sub-views of five functional regions of the mouse brain as depicted in the reconstructed UV-AutoM image of  FIG. 10C . 
         FIG. 11A  is a functional block diagram representing an example computer implemented system for generating a pseudo-stained histological image (i.e. virtual stained histological image). 
         FIG. 11B  is a flowchart representing an example computer implemented method  1150  for generating a pseudo-hematoxylin and eosin (H&amp;E) stained image. 
         FIG. 12  is a functional block diagram representing a detailed workflow of a forward cycle and backward cycle of a Cycle-GAN. 
         FIG. 13  is a functional block diagram representing an example generator of the Cycle-GAN of  FIG. 11 . 
         FIG. 14A  is an example of a grayscale UV-PAM image of a mouse brain slice generated using the system of  FIG. 3  which is provided as input to a trained Cycle-GAN configured according to  FIGS. 11 to 13 . 
         FIG. 14B  is an example of a virtually-stained histological image generated as an output image by the Cycle-GAN using the grayscale UV-PAM image as the input image. 
         FIG. 14C  is an example of a histological image of the same specimen imaged in  FIG. 14A  which was obtained using bright-field microscopy after H&amp;E staining. 
         FIG. 15A  is an example of a UV-AutoM image of a deparaffinized FFPE mouse brain section with 7-μm thickness. 
         FIG. 15B  is a virtual stained version of the UV-AutoM image of  FIG. 15A  utilizing a GAN based network. 
         FIG. 15C  is a bright-field H&amp;E image of the mouse brain section imaged in relation to  FIG. 15A . 
         FIGS. 16A and 16B  are SI reconstructed high-resolution UV-AutoM images, generated with the method of  FIGS. 5B and 5C , showing mouse brain samples with a thickness of 100 μm and 200 μm, respectively. 
         FIGS. 16C and 16D  are examples of virtually-stained H&amp;E images generated using the computer implemented method and system of  FIGS. 11 to 13 . 
         FIG. 17A  shows an example of label-free UV-PAM image of a hippocampus region from a deparaffinized FFPE mouse brain sample with 7-μm thickness, wherein the UV-PAM image was generated using the system of  FIG. 3 . 
         FIG. 17B  shows an example of a UV-AutoM image of the hippocampus region imaged in  FIG. 17A . 
         FIG. 17C  shows an example of bright-field H&amp;E-stained image corresponding to the mouse brain sample imaged in  FIGS. 17A and 17B . 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Where reference is made in any one or more of the accompanying drawings to steps and/or features, which have the same reference numerals, those steps and/or features have for the purposes of this description the same function(s) or operation(s), unless the contrary intention appears. 
     It is to be noted that the discussions contained in the “Background” section and that above relating to prior art arrangements relate to discussions of documents or devices which form public knowledge through their respective publication and/or use. Such should not be interpreted as a representation by the present inventor(s) or the patent applicant that such documents or devices in any way form part of the common general knowledge in the art. 
     Referring to  FIGS. 1A and 1B  there is shown a schematic of an example of a general-purpose computer system  100 , upon which the various arrangements described herein. 
     As seen in  FIG. 1A , the computer system  100  includes: a computer module  101 ; input devices such as a keyboard  102 , a mouse pointer device  103 , a scanner  126 , a camera  127 , and a microphone  180 ; and output devices including a printer  115 , a display device  114  and loudspeakers  117 . An external Modulator-Demodulator (Modem) transceiver device  116  may be used by the computer module  101  for communicating to and from a communications network  120  via a connection  121 . The communications network  120  may be a wide-area network (WAN), such as the Internet, a cellular telecommunications network, or a private WAN. Where the connection  121  is a telephone line, the modem  116  may be a traditional “dial-up” modem. Alternatively, where the connection  121  is a high capacity (e.g., cable) connection, the modem  116  may be a broadband modem. A wireless modem may also be used for wireless connection to the communications network  120 . 
     The computer module  101  typically includes at least one processor unit  105 , and a memory unit  106 . For example, the memory unit  106  may have semiconductor random access memory (RAM) and semiconductor read only memory (ROM). The computer module  101  also includes an number of input/output (I/O) interfaces including: an audio-video interface  107  that couples to the video display  114 , loudspeakers  117  and microphone  180 ; an  1 /O interface  113  that couples to the keyboard  102 , mouse  103 , scanner  126 , camera  127  and optionally a joystick or other human interface device (not illustrated), or a projector; and an interface  108  for the external modem  116  and printer  115 . In some implementations, the modem  116  may be incorporated within the computer module  101 , for example within the interface  108 . The computer module  101  also has a local network interface  111 , which permits coupling of the computer system  100  via a connection  123  to a local-area communications network  122 , known as a Local Area Network (LAN). As illustrated in  FIG. 1A , the local communications network  122  may also couple to the wide network  120  via a connection  124 , which would typically include a so-called “firewall” device or device of similar functionality. The local network interface  111  may comprise an Ethernet circuit card, a Bluetooth® wireless arrangement or an IEEE 802.11 wireless arrangement; however, numerous other types of interfaces may be practiced for the interface  111 . 
     The I/O interfaces  108  and  113  may afford either or both of serial and parallel connectivity, the former typically being implemented according to the Universal Serial Bus (USB) standards and having corresponding USB connectors (not illustrated). Storage devices  109  are provided and typically include a hard disk drive (HDD)  110 . Other storage devices such as a floppy disk drive and a magnetic tape drive (not illustrated) may also be used. An optical disk drive  112  is typically provided to act as a non-volatile source of data. Portable memory devices, such optical disks (e.g., CD-ROM, DVD, Blu ray Disc™), USB-RAM, portable, external hard drives, and floppy disks, for example, may be used as appropriate sources of data to the system  100 . 
     The components  105  to  113  of the computer module  101  typically communicate via an interconnected bus  104  and in a manner that results in a conventional mode of operation of the computer system  100  known to those in the relevant art. For example, the processor  105  is coupled to the system bus  104  using a connection  118 . Likewise, the memory  106  and optical disk drive  112  are coupled to the system bus  104  by connections  119 . Examples of computers on which the described arrangements can be practiced include IBM-PC&#39;s and compatibles, Sun Sparcstations, Apple Mac™ or a like computer system. 
     The methods as described herein may be implemented using the computer system  100  wherein the processes described herein may be implemented as one or more software application programs  133  executable within the computer system  100 . In particular, the steps of the methods described herein are effected by instructions  131  (see  FIG. 1B ) in the software  133  that are carried out within the computer system  100 . The software instructions  131  may be formed as one or more code modules, each for performing one or more particular tasks. 
     The software may be stored in a computer readable medium, including the storage devices described below, for example. The software is loaded into the computer system  100  from the computer readable medium, and then executed by the computer system  100 . A computer readable medium having such software or computer program recorded on the computer readable medium is a computer program product. The use of the computer program product in the computer system  100  preferably effects an advantageous apparatus for detecting and/or sharing writing actions. 
     The software  133  is typically stored in the HDD  110  or the memory  106 . The software is loaded into the computer system  100  from a computer readable medium, and executed by the computer system  100 . Thus, for example, the software  133  may be stored on an optically readable disk storage medium (e.g., CD-ROM)  125  that is read by the optical disk drive  112 . A computer readable medium having such software or computer program recorded on it is a computer program product. 
     In some instances, the application programs  133  may be supplied to the user encoded on one or more CD-ROMs  125  and read via the corresponding drive  112 , or alternatively may be read by the user from the networks  120  or  122 . Still further, the software can also be loaded into the computer system  100  from other computer readable media. Computer readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and/or data to the computer system  100  for execution and/or processing. Examples of such storage media include floppy disks, magnetic tape, CD-ROM, DVD, Blu-ray™ Disc, a hard disk drive, a ROM or integrated circuit. USB memory, a magneto-optical disk, or a computer readable card such as a PCMCIA card and the like, whether or not such devices are internal or external of the computer module  101 . Examples of transitory or non-tangible computer readable transmission media that may also participate in the provision of software, application programs, instructions and/or data to the computer module  101  include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the Internet or Intranets including e-mail transmissions and information recorded on Websites and the like. 
     The second part of the application programs  133  and the corresponding code modules mentioned above may be executed to implement one or more graphical user interfaces (GUIs) to be rendered or otherwise represented upon the display  114 . Through manipulation of typically the keyboard  102  and the mouse  103 , a user of the computer system  100  and the application may manipulate the interface in a functionally adaptable manner to provide controlling commands and/or input to the applications associated with the GUI(s). Other forms of functionally adaptable user interfaces may also be implemented, such as an audio interface utilizing speech prompts output via the loudspeakers  117  and user voice commands input via the microphone  180 . 
       FIG. 1B  is a detailed schematic block diagram of the processor  105  and a “memory”  134 . The memory  134  represents a logical aggregation of all the memory modules (including the HDD  109  and semiconductor memory  106 ) that can be accessed by the computer module  101  in  FIG. 1A . 
     When the computer module  101  is initially powered up, a power-on self-test (POST) program  150  executes. The POST program  150  is typically stored in a ROM  149  of the semiconductor memory  106  of  FIG. 1A . A hardware device such as the ROM  149  storing software is sometimes referred to as firmware. The POST program  150  examines hardware within the computer module  101  to ensure proper functioning and typically checks the processor  105 , the memory  134  ( 109 ,  106 ), and a basic input-output systems software (BIOS) module  151 , also typically stored in the ROM  149 , for correct operation. Once the POST program  150  has run successfully, the BIOS  151  activates the hard disk drive  110  of  FIG. 1A . Activation of the hard disk drive  110  causes a bootstrap loader program  152  that is resident on the hard disk drive  110  to execute via the processor  105 . This loads an operating system  153  into the RAM memory  106 , upon which the operating system  153  commences operation. The operating system  153  is a system level application, executable by the processor  105 , to fulfil various high level functions, including processor management, memory management, device management, storage management, software application interface, and generic user interface. 
     The operating system  153  manages the memory  134  ( 109 ,  106 ) to ensure that each process or application running on the computer module  101  has sufficient memory in which to execute without colliding with memory allocated to another process. Furthermore, the different types of memory available in the system  100  of  FIG. 1A  must be used properly so that each process can run effectively. Accordingly, the aggregated memory  134  is not intended to illustrate how particular segments of memory are allocated (unless otherwise stated), but rather to provide a general view of the memory accessible by the computer system  100  and how such is used. 
     As shown in  FIG. 1B , the processor  105  includes a number of functional modules including a control unit  139 , an arithmetic logic unit (ALU)  140 , and a local or internal memory  148 , sometimes called a cache memory. The cache memory  148  typically includes a number of storage registers  144 - 146  in a register section. One or more internal busses  141  functionally interconnect these functional modules. The processor  105  typically also has one or more interfaces  142  for communicating with external devices via the system bus  104 , using a connection  118 . The memory  134  is coupled to the bus  104  using a connection  119 . 
     The application program  133  includes a sequence of instructions  131  that may include conditional branch and loop instructions. The program  133  may also include data  132  which is used in execution of the program  133 . The instructions  131  and the data  132  are stored in memory locations  128 ,  129 ,  130  and  135 ,  136 ,  137 , respectively. Depending upon the relative size of the instructions  131  and the memory locations  128 - 130 , a particular instruction may be stored in a single memory location as depicted by the instruction shown in the memory location  130 . Alternately, an instruction may be segmented into a number of parts each of which is stored in a separate memory location, as depicted by the instruction segments shown in the memory locations  128  and  129 . 
     In general, the processor  105  is given a set of instructions which are executed therein. The processor  105  waits for a subsequent input, to which the processor  105  reacts to by executing another set of instructions. Each input may be provided from one or more of a number of sources, including data generated by one or more of the input devices  102 ,  103 , data received from an external source across one of the networks  120 ,  102 , data retrieved from one of the storage devices  106 ,  109  or data retrieved from the storage medium  125  inserted into the corresponding reader  112 , all depicted in  FIG. 1A . The execution of a set of the instructions may in some cases result in output of data. Execution may also involve storing data or variables to the memory  134 . 
     The disclosed writing detection and sharing arrangements use input variables  154 , which are stored in the memory  134  in corresponding memory locations  155 ,  156 ,  157 . The writing detection and sharing arrangements produce output variables  161 , which are stored in the memory  134  in corresponding memory locations  162 ,  163 ,  164 . Intermediate variables  158  may be stored in memory locations  159 ,  160 ,  166  and  167 . 
     Referring to the processor  105  of  FIG. 1B , the registers  144 ,  145 ,  146 , the arithmetic logic unit (ALU)  140 , and the control unit  139  work together to perform sequences of micro-operations needed to perform “fetch, decode, and execute” cycles for every instruction in the instruction set making up the program  133 . Each fetch, decode, and execute cycle comprises: a fetch operation, which fetches or reads an instruction  131  from a memory location  128 ,  129 ,  130 ; a decode operation in which the control unit  139  determines which instruction has been fetched; and an execute operation in which the control unit  139  and/or the ALU  140  execute the instruction. 
     Thereafter, a further fetch, decode, and execute cycle for the next instruction may be executed. Similarly, a store cycle may be performed by which the control unit  139  stores or writes a value to a memory location  162 . 
     Each step or sub-process in the processes described herein are associated with one or more segments of the program  133  and is performed by the register section  144 ,  145 ,  147 , the ALU  140 , and the control unit  139  in the processor  105  working together to perform the fetch, decode, and execute cycles for every instruction in the instruction set for the noted segments of the program  133 . 
     The methods described herein may alternatively be implemented in dedicated hardware such as one or more integrated circuits performing the functions or sub functions of the writing detection and sharing methods. Such dedicated hardware may include graphic processors, digital signal processors, or one or more microprocessors and associated memories. 
       FIGS. 2A and 2B  collectively form a schematic block diagram of a general purpose electronic device  201  including embedded components, upon which the writing detection and/or sharing methods to be described are desirably practiced. The electronic device  201  may be, for example, a mobile phone, a portable media player, virtual reality glasses or a digital camera, in which processing resources are limited. Nevertheless, the methods to be described may also be performed on higher-level devices such as desktop computers, server computers, and other such devices with significantly larger processing resources. 
     As seen in  FIG. 2A , the electronic device  201  comprises an embedded controller  202 . Accordingly, the electronic device  201  may be referred to as an “embedded device.” In the present example, the controller  202  has a processing unit (or processor)  205  which is bi-directionally coupled to an internal storage module  209 . The storage module  209  may be formed from non-volatile semiconductor read only memory (ROM)  260  and semiconductor random access memory (RAM)  270 , as seen in  FIG. 2B . The RAM  270  may be volatile, non-volatile or a combination of volatile and non-volatile memory. 
     The electronic device  201  includes a display controller  207 , which is connected to a display  214 , such as a liquid crystal display (LCD) panel or the like. The display controller  207  is configured for displaying graphical images on the display  214  in accordance with instructions received from the embedded controller  202 , to which the display controller  207  is connected. 
     The electronic device  201  also includes user input devices  213  which are typically formed by keys, a keypad or like controls. In some implementations, the user input devices  213  may include a touch sensitive panel physically associated with the display  214  to collectively form a touch-screen. Such a touch-screen may thus operate as one form of graphical user interface (GUI) as opposed to a prompt or menu driven GUI typically used with keypad-display combinations. Other forms of user input devices may also be used, such as a microphone (not illustrated) for voice commands or a joystick/thumb wheel (not illustrated) for ease of navigation about menus. 
     As seen in  FIG. 2A , the electronic device  201  also comprises a portable memory interface  206 , which is coupled to the processor  205  via a connection  219 . The portable memory interface  206  allows a complementary portable memory device  225  to be coupled to the electronic device  201  to act as a source or destination of data or to supplement the internal storage module  209 . Examples of such interfaces permit coupling with portable memory devices such as Universal Serial Bus (USB) memory devices, Secure Digital (SD) cards, Personal Computer Memory Card International Association (PCMIA) cards, optical disks and magnetic disks. 
     The electronic device  201  also has a communications interface  208  to permit coupling of the device  201  to a computer or communications network  220  via a connection  221 . The connection  221  may be wired or wireless. For example, the connection  221  may be radio frequency or optical. An example of a wired connection includes Ethernet. Further, an example of wireless connection includes Bluetooth™ type local interconnection, Wi-Fi (including protocols based on the standards of the IEEE 802.11 family), Infrared Data Association (IrDa) and the like. 
     Typically, the electronic device  201  is configured to perform some special function. The embedded controller  202 , possibly in conjunction with further special function components  210 , is provided to perform that special function. For example, where the device  201  is a digital camera, the components  210  may represent a lens, focus control and image sensor of the camera. The special function component  210  is connected to the embedded controller  202 . As another example, the device  201  may be a mobile telephone handset. In this instance, the components  210  may represent those components required for communications in a cellular telephone environment. Where the device  201  is a portable device, the special function components  210  may represent a number of encoders and decoders of a type including Joint Photographic Experts Group (JPEG), (Moving Picture Experts Group) MPEG, MPEG-1 Audio Layer 3 (MP3), and the like. 
     The methods described hereinafter may be implemented using the embedded controller  202 , where the processes described herein may be implemented as one or more software application programs  233  executable within the embedded controller  202 . The electronic device  201  of  FIG. 2A  implements the described methods. In particular, with reference to  FIG. 2B , the steps of the described methods are effected by instructions in the software  233  that are carried out within the controller  202 . The software instructions may be formed as one or more code modules, each for performing one or more particular tasks. The software may also be divided into two separate parts, in which a first part and the corresponding code modules performs the described methods and a second part and the corresponding code modules manage a user interface between the first part and the user. 
     The software  233  of the embedded controller  202  is typically stored in the non-volatile ROM  260  of the internal storage module  209 . The software  233  stored in the ROM  260  can be updated when required from a computer readable medium. The software  233  can be loaded into and executed by the processor  205 . In some instances, the processor  205  may execute software instructions that are located in RAM  270 . Software instructions may be loaded into the RAM  270  by the processor  205  initiating a copy of one or more code modules from ROM  260  into RAM  270 . Alternatively, the software instructions of one or more code modules may be pre-installed in a non-volatile region of RAM  270  by a manufacturer. After one or more code modules have been located in RAM  270 , the processor  205  may execute software instructions of the one or more code modules. 
     The application program  233  is typically pre-installed and stored in the ROM  260  by a manufacturer, prior to distribution of the electronic device  201 . However, in some instances, the application programs  233  may be supplied to the user encoded on one or more CD-ROM (not shown) and read via the portable memory interface  206  of  FIG. 2A  prior to storage in the internal storage module  209  or in the portable memory  225 . In another alternative, the software application program  233  may be read by the processor  205  from the network  220 , or loaded into the controller  202  or the portable storage medium  225  from other computer readable media. Computer readable storage media refers to any non-transitory tangible storage medium that participates in providing instructions and/or data to the controller  202  for execution and/or processing. Examples of such storage media include floppy disks, magnetic tape, CD-ROM, a hard disk drive, a ROM or integrated circuit, USB memory, a magneto-optical disk, flash memory, or a computer readable card such as a PCMCIA card and the like, whether or not such devices are internal or external of the device  201 . Examples of transitory or non-tangible computer readable transmission media that may also participate in the provision of software, application programs, instructions and/or data to the device  201  include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the Internet or Intranets including e-mail transmissions and information recorded on Websites and the like. A computer readable medium having such software or computer program recorded on it is a computer program product. 
     The second part of the application programs  233  and the corresponding code modules mentioned above may be executed to implement one or more graphical user interfaces (GUIs) to be rendered or otherwise represented upon the display  214  of  FIG. 2A . Through manipulation of the user input device  213  (e.g., the keypad), a user of the device  201  and the application programs  233  may manipulate the interface in a functionally adaptable manner to provide controlling commands and/or input to the applications associated with the GUI(s). Other forms of functionally adaptable user interfaces may also be implemented, such as an audio interface utilizing speech prompts output via loudspeakers (not illustrated) and user voice commands input via the microphone (not illustrated). 
       FIG. 2B  illustrates in detail the embedded controller  202  having the processor  205  for executing the application programs  233  and the internal storage  209 . The internal storage  209  comprises read only memory (ROM)  260  and random access memory (RAM)  270 . The processor  205  is able to execute the application programs  233  stored in one or both of the connected memories  260  and  270 . When the electronic device  201  is initially powered up, a system program resident in the ROM  260  is executed. The application program  233  permanently stored in the ROM  260  is sometimes referred to as “firmware”. Execution of the firmware by the processor  205  may fulfil various functions, including processor management, memory management, device management, storage management and user interface. 
     The processor  205  typically includes a number of functional modules including a control unit (CU)  251 , an arithmetic logic unit (ALU)  252 , a digital signal processor (DSP)  253  and a local or internal memory comprising a set of registers  254  which typically contain atomic data elements  256 ,  257 , along with internal buffer or cache memory  255 . One or more internal buses  259  interconnect these functional modules. The processor  205  typically also has one or more interfaces  258  for communicating with external devices via system bus  281 , using a connection  261 . 
     The application program  233  includes a sequence of instructions  262  through  263  that may include conditional branch and loop instructions. The program  233  may also include data, which is used in execution of the program  233 . This data may be stored as part of the instruction or in a separate location  264  within the ROM  260  or RAM  270 . 
     In general, the processor  205  is given a set of instructions, which are executed therein. This set of instructions may be organised into blocks, which perform specific tasks or handle specific events that occur in the electronic device  201 . Typically, the application program  233  waits for events and subsequently executes the block of code associated with that event. Events may be triggered in response to input from a user, via the user input devices  213  of  FIG. 2A , as detected by the processor  205 . Events may also be triggered in response to other sensors and interfaces in the electronic device  201 . 
     The execution of a set of the instructions may require numeric variables to be read and modified. Such numeric variables are stored in the RAM  270 . The disclosed method uses input variables  271  that are stored in known locations  272 ,  273  in the memory  270 . The input variables  271  are processed to produce output variables  277  that are stored in known locations  278 ,  279  in the memory  270 . Intermediate variables  274  may be stored in additional memory locations in locations  275 ,  276  of the memory  270 . Alternatively, some intermediate variables may only exist in the registers  254  of the processor  205 . 
     The execution of a sequence of instructions is achieved in the processor  205  by repeated application of a fetch-execute cycle. The control unit  251  of the processor  205  maintains a register called the program counter, which contains the address in ROM  260  or RAM  270  of the next instruction to be executed. At the start of the fetch execute cycle, the contents of the memory address indexed by the program counter is loaded into the control unit  251 . The instruction thus loaded controls the subsequent operation of the processor  205 , causing for example, data to be loaded from ROM memory  260  into processor registers  254 , the contents of a register to be arithmetically combined with the contents of another register, the contents of a register to be written to the location stored in another register and so on. At the end of the fetch execute cycle the program counter is updated to point to the next instruction in the system program code. Depending on the instruction just executed this may involve incrementing the address contained in the program counter or loading the program counter with a new address in order to achieve a branch operation. 
     Each step or sub-process in the processes of the methods described below is associated with one or more segments of the application program  233 , and is performed by repeated execution of a fetch-execute cycle in the processor  205  or similar programmatic operation of other independent processor blocks in the electronic device  201 . 
     Aspects provide a high-throughput, label-free, and slide-free imaging method and system based on intrinsic optical absorption contrast under ultraviolet light illumination to probe histologically-stained biomolecules directly. Two approaches are disclosed, namely ultraviolet-based (i) photoacoustic microscopy (UV-PAM), and (ii) autofluorescence microscopy (UV-AutoM). To achieve high throughput for UV-PAM, a high-speed optical scanning configuration can be utilized. In relation to UV-AutoM, speckle illumination (SI) is utilized which allows estimation of high-resolution images with a low-magnification objective lens, providing images with subcellular resolution across centimeter-scale imaging area, whilst simultaneously allowing high tolerance to tissue surface morphology, slide placement errors and thickness-induced image blur. 
     For both types of images, UV-PAM and UV-AutoM, a deep learning-based virtual staining method and system is disclosed which can be used to generate histology-like images of large unprocessed fresh/fixed tissues at subcellular resolution. The virtual staining method and system utilizes a generative adversarial network (GAN), which is configured to enable transformation of a UV-PAM or UV-AutoM image of an unlabeled tissue into a histologically-stained image through paired/unpaired training examples. The disclosed method and system can simplify the workflow of standard-of-care histopathology from days to less than ten minutes, enabling intraoperative surgical margin assessment, thereby reducing or eliminating the need of second surgeries due to positive margin. 
     Ultraviolet-Based Photoacoustic Microscopy (UV-PAM) 
     Unlike conventional optical microscopy, PAM takes the advantage of optical absorption contrast, which is highly specific. By using a UV pulsed laser (wavelengths ranging from ˜240-280 nm) as an excitation beam, cell nuclei can be highlighted, thus providing label-free histology-like images. 
     Referring to  FIG. 3  there is shown an example of a UV-PAM system  300 . In particular, a nanosecond pulsed UV laser  302  (for example, WEDGE-HF 266 nm, available from Bright Solution Srl.) is focused by a focusing assembly  303 . In particular, the light emitted by the UV laser  302  is expanded by a pair of lenses  304 ,  308  (for example, LA4647-UV and LA4663-UV, available from Thorlabs Inc.). The quality of the UV light beam can be improved by a pinhole  306  located between the pair of lenses  304 ,  308 . The size of the pinhole  306  may be 10 μm to 100 μm in diameter, (for example, P25C, available from Thorlabs Inc.). The beam is subsequently reflected by a 1D galvo-mirror scanner  310  and then focused on the bottom of a specimen  312  by an objective lens  310  (for example, a MicroSpot™ Focusing Objective (LMU-20X-UVB), Thorlabs Inc.). The specimen  312  is placed on the bottom of a water tank  314  which is held by a sample holder  315  attached to an XYZ translation stage  318 . The water tank  314  is filled with water to allow the photoacoustic waves to propagate upward and to be detected by a water-immersed ultrasonic transducer  316  (for example, V324-SU, available fdrom Olympus NDT Inc.). 
     The received acoustic pressure is converted to electric signals, then amplified by amplifiers  320  (for example, two ZFL-500LN-BNC+, available from Mini-circuits Inc.) and last, received by a computer system  100  or  201  through a data acquisition system  322  (for example, ATS9350, available from Alazar Technologies Inc.). To generate a two-dimensional image, the maximum amplitude projection (MAP) of each A-line signal is first identified. The maximum amplitude projections are then rearranged according to the order in the scanning process to generate grayscale images. 
     In operation, the galvo-mirror scanner  310  of the focusing assembly can be controlled to focus ultraviolet light on the specimen  312  according to a scanning trajectory. The controlling of the galvo-mirror scanner  310  can be performed by the computer system  100  or part of a computerised embedded device  201 . The transducer  316  is configured to receive the photoacoustic waves emitted by the specimen  312  in response to ultraviolet light. The computer system  100  or embedded device  201  generates, based on the photoacoustic waves, the UV-PAM image. 
     In order to measure the lateral and axial resolutions of the UV-PAM system, gold nanoparticles (200-nm diameter) were imaged with a step size of 0.15 μm on both x- and y-axis ( FIG. 4( a ) ). The data points of four gold nanoparticles were selected and averaged to measure the lateral resolution by Gaussian profile fitting as shown in  FIG. 4( b ) . The full width at half maximum (FWHM) of the Gaussian fitting profile is ˜0.6 μm. To evaluate the axial resolution, the envelope of the A-line signal of the central position can be extracted.  FIG. 4( c )  shows the A-line signal of the central position of the gold nanoparticle in (a). The FWHM of the envelope of the A-line signal is 38 ns, which corresponds to 58 μm, representing the axial resolution of the UV-PAM system. 
     Ultraviolet-Based Autofluorescence Microscopy (UV-AutoM) 
     In histopathological examination, an objective lens with a 20×-40× magnification factor is typically required to achieve a subcellular resolution for the observation of cellular morphology and metabolic activities. However, such a magnification factor restricts the field-of-view (FOV) to within 1 mm 2 . In addition, high magnification objective lens suffers more from spatially-varied aberrations and features a shallow depth-of-field (DOF) which leads to low tolerance to placement errors of microscope slide and specimen roughness. For such reasons, capturing a large tissue surface via image stitching with a high magnification objective lens is sub-optional. 
     Disclosed is a speckle illumination (SI) method to alleviate inherent tradeoffs between a large FOV and high resolution (HR) in digital microscopes, enabling a high-throughput visualization of different regions of interest with subcellular resolution. 
     As illustrated in  FIG. 5A  showing an example UV-AutoM system  500 , an unstained fresh tissue  312  is placed on an open-top sample holder which is connected to an XYZ motorized stage  318  (for example, three L-509, available from PI miCos, GmbH). A UV laser  302  (for example, WEDGE-HF 266 nm, available from Bright Solution Srl.) is collimated and projected onto a fused silica diffuser (for example, DGUV10-600, available from Thorlabs Inc.) to generate a speckle pattern. The resulting wave is focused through an aspherical UV condenser lens  512  with a numerical aperture (NA) of 0.69 (for example, #33-957, available from Edmund Optics Inc.) as part of the focusing assembly  303 . The sample  312  is obliquely illuminated through a UV transparent window  514  (e.g. transmission greater than 90% from 200 nm to 1500 nm). The excited autofluorescence signal (mainly from NADH and FAD, peak emission at 450 nm) is then collected by an inverted microscope  517  equipped with a 4× objective lens  518  (e.g. NA=0.1, plan achromat objective, available from Olympus NDT Inc.) and an infinity-corrected tube lens  520  (TTL180-A, available from Thorlabs Inc.), and finally imaged by a monochrome scientific complementary metal-oxide semiconductor (sCMOS) camera  522  (for example, a PCO edge 4.2, 6.5 μm pixel pitch, available from PCO AG). 
     A low magnification objective lenses suffers less from spatially-varied aberrations across a large FOV, and features larger DOF and longer working distance, which allows high tolerance to slide placement errors and enables flexible operations on the sample stage. However, its spatial resolution is largely restricted by the low NA value, which is the determining factor for the achievable resolution of an imaging system according to Rayleigh criterion (i.e. the minimum distance that an imaging system can resolve is 0.61λ/NA, where X is the fluorescence emission wavelength and NA is the objective lens&#39;s numerical aperture). To this end, SI reconstruction is utilized to bypass such a resolution-limit set by a low-NA objective lens in this configuration. 
     Disclosed is a computational imaging method based on speckle illumination to achieve autofluorescence microscopy. In a preferred embodiment, this method achieves high-throughput microscopy. In particular, “high-throughput microscopy” refers to the use of automated microscopy and image analysis to visualize and quantitatively capture cellular features at a large scale. More specifically, the spatial-bandwidth product (i.e. field-of-view/resolution 2 ) of the high-throughput output due to the application of speckle illumination is about or equal to 10 times more than conventional fluorescence microscopy which is typically constrained to megapixels level. A low-magnification objective lens is favored for imaging large tissue surface since it suffers less from spatially-varied aberrations. In addition, out-of-focus image blur caused by surface irregularity, tissue thickness, or slide placement errors can be minimized through the implementation of a low-magnification lens since their large depth-of-field. However, the low numerical aperture (NA) value of such lens largely restricts the achievable resolution, thus hinder their applications where the target on subcellular-level imaging. The proposed method performs an iterative reconstruction through a sequence of speckle-illuminated low-resolution autofluorescence images to bypass the resolution limit set by a low-NA objective lens, facilitating fast high-resolution imaging across a large imaging area with arbitrary surface morphology. 
     Referring to  FIGS. 5B and 5C  there is shown flowchart representing an example computer-implemented method  530  of reconstructing a UV-excited autofluorescence image (UV-AutoM) from a sequence of speckle-illuminated images. The computer-implemented method  530  can be performed by a computer system  100  as described in relation to  FIGS. 1A and 1B  or by an embedded device  201  as described in relation to  FIGS. 2A and 2B .  FIG. 5D  provides pseudocode further representing a more specific implementation of reconstructing a high-throughput UV-excited autofluorescence image (UV-AutoM) from a sequence of speckle-illuminated images. The flowchart of  FIGS. 5B and 5C  will be described herein with reference to the pseudocode of  FIG. 5D . 
     In particular, at step  530 - 1 , the method  530  includes recording a sequence of speckle illuminated images I j (j=1,2, . . . , N) of a specimen translated to a respective sequence of positions in a plane along a scanning trajectory. The sequence of speckle illuminated images I are low resolution images in the sense that the output of method  530  is a high resolution image which has a higher resolution compared to each of the low resolution speckle illuminated images I j . In one example, the sequence of speckle illuminated images could be captured using a 4×/0.1NA objective lens. 
     At step  530 - 2 , the method  530  includes initializing an image object o(x,y), herein referred to as a high resolution image object, and a speckle pattern. As shown in line  3  of the pseudocode, the sequence of speckle illuminated images are averaged and the averaged speckle illuminated image is interpolated, wherein the high resolution image object o(x,y) is set to the result of the interpolation of the averaged speckle illuminated image. The speckle pattern is initialized to a matrix of ones. 
     At step  530 - 3 , a current position is set to a first position from the sequence of positions. The current position in the pseudocode of  FIG. 5D  is represented by (x j ,y j ). 
     Steps  530 - 4  to  530 - 11  are performed for each speckle-illuminated image in the captured sequence in the form of an inner loop of the flowchart illustrated in  FIGS. 5B and 5C . Referring to the pseudocode of  FIG. 5D , the inner loop represents lines  6  to  14 , and an outer loop is represented by lines  5  to  17 . For the inner loop, effectively the current position variable is incremented to the next position in the sequence of positions and the corresponding speckle illuminated image at the respective current position is used in image processing steps to modify the high resolution image object and the speckle pattern. 
     More specifically, at step  530 - 4  the method  530  includes generating an estimated speckle illuminated image φ j (x,y) by computationally shifting the high resolution object to the current position o(x−x j ,y−y j ) which is then multiplied by the speckle pattern p(x,y). This is shown in line  7  of the pseudocode of  FIG. 5D . 
     At step  530 - 5 , the method  530  includes determining a filtered object-pattern compound, ψ j (k x ,k y ), in the frequency domain based on the estimated speckle illuminated image in the frequency domain, F(φ j (x,y)), which is multiplied with an optical transfer function, OTF(k x , k y ) where kc and ky are spatial coordinates in the frequency domain. The optical transfer function is the known optical transfer function of the apparatus used for capturing the sequence of speckle illuminated images of the specimen. 
     It is noted that the shifting operation in steps  530 - 4  and  530 - 5  are collectively an application of angular spectrum. 
     Steps  530 - 6  to  530 - 8  described below are reconstruction procedures based on a phase retrieval algorithm termed Ptychographic iterative engine (PIE). 
     At step  530 - 6 , the method  530  includes determining an updated estimated speckle illuminated image in the frequency domain, F(φ j   update ), based on the estimated speckle illuminated image in the frequency domain, the captured speckle illuminated image at the current position in the frequency domain, the filtered object-pattern compound in the frequency domain, the optical transfer function, and an adaptive learning rate parameter a. More specifically, line  9  of the pseudocode of the  FIG. 5D  shows the specific calculation for the updated estimated speckle illuminated image in the frequency domain which is shown by Equation 1 below: 
         F (φ j   update )= F (φ j )+α*conj(OTF)*[ F ( I   j )−ψ j ]/|OTF| max   2   Equation 1
 
     In particular, the updated estimated speckle illuminated image in the frequency domain is calculated to equal the estimated speckle illuminated image in the frequency domain summed with to the adaptive learning rate parameter multiplied by a conjugate of the optical transfer function multiplied by a difference between the captured speckle illuminated image at the current position in the frequency domain and the filtered object-pattern compound in the frequency domain divided by the square of the absolute maximum value of the optical transfer function. 
     At step  530 - 7 , the method  530  includes updating the high resolution object based on the updated estimated speckle illuminated image in the spatial domain, and the speckle pattern. This is shown in line  10  of the pseudocode of  FIG. 5D  and represented by Equation 2 below: 
         o ( x−x   j   ,y−y   j )= o ( x−x   j   ,y−y   j )+conj( p )*(φ j   update −φ j )/| p|   max   2   Equation 2
 
     In particular, the high resolution object is set to equal the high resolution object summed with the conjugate of the speckle pattern multiplied by the difference between the updated estimated speckle illuminated image in the spatial domain and the estimated speckle illuminated image in the spatial domain divided by the square of the absolute value of the maximum value of the speckle pattern. 
     At step  530 - 8 , the method  530  includes updating the speckle pattern based on the updated estimated speckle illuminated image, the estimated speckle illuminated image, and the high resolution object. This is shown in line  11  of the pseudocode of  FIG. 5D  and represented by Equation 3 below: 
         p=p +conj( o )*(φ j   update −φ j )/| o|   max   2   Equation 3
 
     In particular, the speckle pattern is set to equal the speckle pattern summed with the conjugate of the high resolution object multiplied by the difference between the updated estimated speckle illuminated image in the spatial domain and the estimated speckle illuminated image in the spatial domain divided by the square of the absolute value of the maximum value of the high resolution object. 
     At step  530 - 9 , a summed loss parameter, loss; is calculated for the current loop based on the absolute value of the difference between the captured speckle illuminated image I j  and the inverse Fourier transformation of the filtered object-pattern compound in the frequency domain ψ j . This is shown in line  12  of the pseudocode of  FIG. 5D  and represented by Equation 4 below: 
       loss j =Σ j   |I   j   −F   −1 (ψ j )|  Equation 4
 
     At step  530 - 10 , the method  530  includes applying Nesterov momentum acceleration to the high resolution object and the speckle pattern. This is performed to accelerate gradient decent of the reconstruction process for faster convergence. 
     At step  530 - 11 , the method  530  includes determining if the current position is the last position in the sequence of positions. In response to a positive determination (i.e. ‘yes’), the method proceeds to step  530 - 13 . In response to a negative determination (i.e. ‘no’), the method then proceeds to step  530 - 12 . 
     At step  530 - 12 , the method  530  includes setting the current position to the next position in the sequence of positions. The method  530  then proceeds to back to step  530 - 4  to perform one or more further iterations of the inner loop represented by steps  530 - 4  to  530 - 11  until the last speckle illuminated image has been processed. 
     At step  530 - 13 , the method  530  includes determining if convergence has been detected based on the summed loss parameter. This is determined by determining a loss ratio calculated based on a difference between the summed loss parameter calculated for the previous and current iteration of the inner loop (i.e. steps  530 - 4  to  530 - 11 ) divided by the summed loss parameter for the previous iteration of the inner loop. The loss ratio is then compared to a loss threshold, which in the example pseudocode of  FIG. 5D  is set to 0.01. If the loss ratio is less than or equal to the loss threshold, the adaptive learning rate parameter a is reduced, in this example the adaptive learning rate parameter a is halved. The adaptive learning rate parameter a is adjusted to suppress oscillation of the loss function near the converging point to minimize the artifacts in the reconstructed image. The reconstruction process will end once the learning rate a is reduced to zero, such that a high-resolution object has been determined and output at the step  530 - 14 . The high resolution image object that is output is a high-resolution UV-AutoM image with enhanced subcellular resolution across centimeter-scale imaging area which has increased resolution compared to each speckle illuminated low resolution image provided as input to the reconstruction method  530 . The speckle pattern which was initially unknown at the start of method  530  is also determined at step  530 - 14 . 
     This computational imaging method represented by method  530  synthesizes a sequence of speckle-illuminated low-resolution images to reconstruct a high-resolution autofluorescence image (UV-AutoM). The method is achieved through a series of updating processes in the spatial domain and the frequency domain. The method begins with an initial guess of the high-resolution object. The object is firstly multiplied with the speckle pattern, Fourier transformed to frequency domain and low-pass filtered by the optical transfer function. Then the filtered spectrum is inverse transformed to the spatial domain with intensity replaced by the corresponding speckle-illuminated low-resolution image. Finally this updated autofluorescence image is transformed to the frequency domain and further updated. One iteration is completed until all the captured low-resolution images are involved, and Nesterov momentum acceleration is implemented for faster gradient descent. A high-resolution UV-AutoM image is output after several iterations, with enhanced subcellular resolution across centimetre-scale image area. The prior knowledge of the speckle pattern is not required, only the relative shift between each low-resolution image should be known. 
     A high-resolution UV-AutoM image will be output after several iterations, with enhanced subcellular resolution across centimeter-scale image area. The prior knowledge of the speckle pattern is not required, only the relative shift (x j , y j ) between each captured image should be known. There suggests that sufficient scanning range (larger than ˜2 low-NA diffraction-limited spot size) and finer scanning steps (smaller than the targeted resolution) can reduce distortions in the reconstruction, and the final achievable NA is the sum of objective&#39;s NA and speckle NA. 
       FIG. 6  shows a graphical representation of an example of an SI reconstruction method and system described previously in relation to  FIGS. 5A to 5C .  FIG. 6  shows at  610  physical constraint set by a 4×/0.1NA objective in the Fourier space and the corresponding low resolution raw image of a mouse brain sample (100-μm thickness). An SI dataset  620  is acquired using the 4×/0.1NA objective as shown in  FIG. 6  which in this example comprises of 49 speckle-illuminated low-resolution measurements, captured by translating the sample to 49 different positions in X-Y plane with 500-nm scanning step size. The SI dataset can then be used to reconstruct a high-throughput UV-AutoM image  630  with an extended passband up to NA=0.3, which corresponds to 3 times resolution enhancement compared with the images acquired using the 4×/0.1NA objective. 
     Fluorescence nanoparticles with a diameter of 500 nm (excitation/emission: 365 nm/445 nm, B500, available from Thermo Fisher) can be used to quantify resolution performance of the SI reconstruction method and system described above. Referring to  FIG. 7A  there is shown an example of a low-resolution fluorescence image captured with a 4×/0.1NA objective under uniform illumination, while  FIG. 7B  is a reconstructed high resolution fluorescence image through 49 speckle-illuminated low-resolution images that raster scanned with a step size of 500 nm. Both  FIGS. 7A and 7B  are captured using a 266-nm UV laser.  FIG. 7C  is an intensity plot along the solid lines  700 ,  710  indicated in  FIGS. 7A and 7B , from which we can quantify the resolution enhancement by the disclosed SI reconstruction method and system. 
     Advantageously, the SI reconstruction method and system can be highly tolerant to a rough surface. To demonstrate high tolerance to rough surface of our system,  FIGS. 8A-8C  shows UV-AutoM images of two unprocessed leaf samples.  FIGS. 8A and 8B  shows low resolution autofluorescence images captured with a 4×/0.1NA objective, while  FIGS. 8C and 8D  shows high resolution SI reconstructed UV-AutoM images through 49 speckle-illuminated low-resolution images scanned with a step size of 500 nm.  FIGS. 8E and 8F  show the corresponding high resolution reference images captured with a 10×/0.3NA objective, presenting obvious out-of-focus regions compared with  FIGS. 8C and 8D  due to the shallow DOF of high-NA objective, demonstrating that the high resolution UV-AutoM image reconstructed by SI method via a low-NA objective can far outperform high-NA objective especially when dealing with a rough surface. 
     Referring to  FIGS. 9A to 9F , there is shown structural matching between UV-AutoM and bright-field H&amp;E-stained images.  FIG. 9A  is a UV-AutoM image of the whole mouse brain slide (FFPE section after deparaffinization, 4-μm thickness),  FIGS. 9B and 9C  are zoomed-in images of boxes  910  and  920  in  FIG. 9A  respectively, while  FIGS. 9D and 9E  are respectively the corresponding bright-field H&amp;E histological images captured by a digital slide scanner (NanoZoomer SQ, Hamamatsu). It can be seen that the cells concentrated at margin (box  910 ) and hippocampus (box  920 ) areas are clearly resolved in the UV-AutoM images with negative contrast, i.e. appear black on images, and demonstrate substantially perfect structural matching (both cell morphology and distribution) with the H&amp;E-stained images. 
     Referring to  FIG. 10A to 10G  there is shown a high-throughput label-free visualization of a thick mouse brain (100-μm thickness, cut by a Leica vibratome). Due to the shallow penetration depth of UV light, the excited autofluorescence is superficially localized within a few microns below the surface, thus allowing label-free and slide-free imaging of thick specimen. Furthermore, application of the SI reconstruction method and system disclosed above to achieve rapid high-resolution visualization of the whole mouse brain via a 4× objective lens, results in less imaging aberrations and allows higher tolerance to rough tissue surface compared with 10× objective lens.  FIG. 10A  shows an example image of a top view of a mouse brain.  FIGS. 10B and 10C  are examples of reconstructed UV-AutoM images each depicting a cross-sectional cut through respective positions (lines A-A and B-B) of the mouse brain of  FIG. 10A  wherein each reconstructed UV-AutoM image is generated using an SI dataset comprising of 441 speckle-illuminated low-resolution images. The exposure time was 200 ms for each low resolution raw image, enabling fast imaging with a total acquisition time within 3 minutes.  FIGS. 10D to 10H  show high-throughput sub-views of five functional regions in  FIG. 10C , including (a) corpus callosum  1010 , (b) hippocampus  1020 , (c) globus pallidus  1030 , (d) caudoputamen  1040 , and (e) parietal cortex  1050 . 
     Generation of Pseudo-Stained Histological Images 
     Pathologists are typically trained for examining histologically-stained tissue samples to make diagnostic decisions. However, both UV-PAM and UV AutoM images are grayscale images. To address or alleviate this issue, a deep-learning based virtual staining method, utilising a generative adversarial network (GAN), is disclosed for transforming a UV-PAM or UV-AutoM image of an unlabeled tissue into a pseudo-hematoxylin and eosin (H&amp;E) stained image. 
     The GAN allows virtual staining of unpaired UV-PAM/UV-AutoM and H&amp;E images, thereby largely simplifying the image pre-processing procedure, which is difficult as tissue may be rotated or deformed during the staining process. A paired training method can also be employed with the UV-PAM/UV-AutoM images with the corresponding H&amp;E-stained images in order to perform pseudo-coloring. 
     In certain embodiments, the UV-PAM image generated using the method and system described in relation to  FIG. 3  and the SI reconstruction UV-AutoM images generated using the system and method of  FIGS. 5A, 5B and 5C  can be provided as input to the trained cycle-GAN in order to generate pseudo-stained histological images. Additionally or alternatively, UV-PAM images generated using the system and method disclosed in US Patent Application No. 2014/0356897, the contents of which is herein incorporated by reference in its entirety, can be used as input to the GAN to generate the pseudo-stained histological images. 
     Referring to  FIG. 11A  there is shown a block diagram representing an example computer implemented system for generating a pseudo-stained histological image (i.e. virtual stained histological image). The computer implemented system can be implemented using computer system  100  or embedded system  201 . 
     The computer implemented system utilizes a generative adversarial network  1100  (GAN) which can be provided in the form of a cycle-consistent generative adversarial network (Cycle-GAN). The cycle-GAN  1100  comprises of four deep convolutional neural networks, namely a first generator module G, a second generator module F, a first discriminator module X and a second discriminator module Y. 
     As shown in  FIG. 11A , generator G is configured to learn to transform UV-AutoM/UV-PAM input images  1110  (exemplified as a UV-PAM image) to bright-field H&amp;E images  1120  (exemplified as a BR-HE image) while generator F is configured to learn to transform bright-field H&amp;E images  1120  to UV-AutoM/UV-PAM images  1110 . Discriminator X is configured to distinguish between real UV-PAM images and fake UV-PAM images produced by generator F, as shown by output  1130 , and at the same time, discriminator Y is configured to discriminate between real bright-field H&amp;E images and fake bright-field H&amp;E images produced by generator G, as shown as output  1140 . Once the generator G can produce H&amp;E images that the discriminator Y cannot distinguish from input real H&amp;E images, the generator G has learned this transformation from UV-AutoM/UV-PAM images to H&amp;E images. Similarly, once the generator F can produce UV-AutoM/UV-PAM images that the discriminator X cannot distinguish from real H&amp;E images, the generator F has learned this transformation from H&amp;E images to UV-AutoM/UV-PAM images. 
     Referring to  FIG. 11B  there is shown a flowchart representing an example computer implemented method  1150  for generating a pseudo-hematoxylin and eosin (H&amp;E) stained image. The computer implemented method can be performed by computer system  100  or embedded system  201 . 
     At step  1160 , the method  1150  includes receiving an input image. The input image is an ultraviolet-based autofluorescence microscopy (UV-AutoM) image or an ultraviolet-based photoacoustic microscopy (UV-PAM) image of an unlabeled specimen. The input image is a grayscale image. 
     At step  1170 , the method  1150  includes transforming the input image, using the generative adversarial network, to a pseudo-H&amp;E stained image of the input image. 
     At step  1180 , the method  1150  includes outputting the pseudo-H&amp;E stained image. 
     Preferably, the generative adversarial network is a generative adversarial network with cycle consistency. 
     In certain implementations, the method  1150  includes training the generative adversarial network using unpaired input and H&amp;E stained images. 
     Referring to  FIG. 12  there is shown a functional block diagram representing a detailed workflow  1200  of a forward cycle  1202  and backward cycle  1204  of the cycle-GAN  1100  of  FIG. 11 . The cycle generative adversarial network comprises of four deep convolutional neural networks including: a first generator G configured to transform the input image to a generated H&amp;E image; a second generator F configured to transform a H&amp;E image to a generated UV-AutoM or UV-PAM image; a first discriminator Y configured to discriminate between a H&amp;E image of a training set and a generated H&amp;E image generated by the first generator deep convolutional neural network; and a second discriminator X configured to discriminate between a UV-AutoM or UV-PAM image of the training set and a generated UV-AutoM or UV-PAM image generated by the second generator deep convolutional neural network. 
     More specifically, the forward cycle  1202 , which shown in the top row of the schematic shown in  FIG. 12 , after inputting one UV-AutoM/UV-PAM image  1210  (herein referred to as the input image) to generator G, wherein the generator G outputs a generated H&amp;E image  1220 . Discriminator Y is configured to determine if the generated H&amp;E image  1220  is real or fake (i.e. the Discriminator Y is configured to identify if the H&amp;E images received from generator G is originated from input real H&amp;E images or from generator G). In turn, the generated H&amp;E image  1220  of the generator G is provided as input to the generator F to be transformed back to a UV-AutoM/UV-PAM image  1230 , which is referred to as the cyclic image  1230 . The loss between the input image  1210  and cyclic image  1230  is referred to as cycle-consistency loss in cycle-GAN. As shown in a bottom row  1204  of the schematic of  FIG. 12 , the backward cycle is symmetrical to the forward cycle. The backward cycle starts from the input H&amp;E image  1240 , wherein the backward cycle learns to transform BR-H&amp;E images  1240  to UV-AutoM/UV-PAM image images  1250 . Similarly, discriminator X is configured to determine if the generated UV-AutoM/UV-PAM image is real or fake by comparing it with the input image. 
     Referring to  FIG. 13  which shows a functional block diagram representing an example generator  1300  of the Cycle-GAN of  FIG. 11A . In one form, the first and second generator, generator G and F, can be configured as Resnet-based generator networks. In alternate embodiments, generator G and F can be configured as U-Net-based generator networks. Each Resnet-based generator can be composed of several down-sampling layers  1320 A,  1330 A,  1330 B,  13330 C, residual blocks  1310 A- 1310 I, and up-sampling layers  1330 D,  1330 E and  1320 B. In the example shown in  FIG. 13 , 9 residual blocks  1310 A- 1310 I are to train the UV-PAM and HE images, each with a pixel number of 256×256. Spatial reflection padding (3×3) is added in the beginning of the neural networks to ensure that the input and output of the respective generator neural network have the same size. Followed by the padding layer  1320 A, each Resnet based generator  1300  includes a down-sampling pathway which includes three Convolution-InstanceNorm-ReLU layers  1330 A,  1330 B and  1330 C. In particular, the first Convolution-InstanceNorm-ReLU down-sampling layer  1330 A has a larger receptive field with a kernel size 7×7 while the other two layers  1330 B,  1330 C have a smaller receptive field with the kernel size 3×3. The image size is recovered to the original image size and the channel is increased to 64 after the first layer  1330 A. The image size is downgraded by a factor of 2 while the channel number is increased by 2 times when it passes the other two layers  1330 B,  1330 C. Followed by the downsampling layers  1320 A,  1330 A,  1330 B,  1330 C, it is a long residual neural network including 9 residual blocks  1310 A- 1310 I. The image size and channel number remains unchanged (256×64×64) when it passes each residual block. After the 9 residual blocks  1310 A- 1310 I, the generator  1300  includes an up-sampling pathway including two Convolution-InstanceNorm-ReLU layers  1330 D,  1330 E with kernel size 3×3 and a reflection padding layer  1320 B (3×3) coupled with Convolution-InstanceNorm-ReLU layer  1340  with kernel size 7×7. The image size is increased by a factor of 2 while the channel number is decreased by half after each up-sampling layer. After two up-sampling layers, the image size is recovered to the original image size and the channel number is decreased to 64. The last coupled layer  1340  is configured to keep the image size unchanged while decreasing the channel number to 3. 
     In a specific configuration, the discriminator networks Dx and Dy can be provided by 70×70 PatchGAN discriminators, which include four 4×4 Convolution-InstanceNorm-LeakyReLU layers. The PatchGAN will produce a 1-dimensional output (real or fake) after the last layer. 
     The described GAN was implemented using Python version 3.7.3, with Pytorch version 1.0.1. The software was implemented on a desktop computer with a Core i7-8700K CPU at 3.7 GHz and 32 GB of RAM, running an Ubuntu 18.04.2 LTS operation system. The training and testing of the Cycle-GAN neural networks were performed using a GeForce GTX 1080Ti GPUs with 11 GB RAM. However, it will be appreciated that other computer systems or embedded systems  201  can be utilized. 
     Examples of virtual stained histological images generated using the above described Cycle-GAN will herein be discussed. 
     Referring to  FIGS. 14A to 14C , a virtually-stained image of a UV-PAM image of a mouse brain slices were generated using the above described UV-PAM method and system and transformed using the above-described Cycle-GAN  1100 . The UV-PAM image and virtually-stained image of the UV-PAM image were compared with histological images of the same sample after H&amp;E staining. In particular, a section of specimen was imaged, which was 7 μm in thickness and was cut from a FFPE mouse brain, wherein the disclosed UV-PAM method and system was operated using raster scanning with a step size of 0.63 μm. As the UV-PAM method and system is configured to generate grayscale images, the grayscale UV-PAM image, as shown in  FIG. 14A  is then transformed to virtually-stained image, as shown in  FIG. 14B , using the Cycle-GAN  1100  described in relation to  FIGS. 11 to 13 . To evaluate whether the generated virtually-stained image can provide similar information as the conventional histological image, a histological image of the same specimen was obtained using bright-field microscopy after H&amp;E staining, as illustrated in  FIG. 14C . On inspection of the color of the cell nuclei and other connective tissues on the virtually-stained image generated by the UV-PAM system, it was determined that the virtually-stained image generated by the UV-PAM system was substantially similar to the conventional histological image. Overall, the UV-PAM system combined with the deep learning system provided in the form of the disclosed Cycle-GAN  1100  generates substantially accurate virtually stained histological images without requiring conventional staining techniques. 
       FIGS. 15A to 15C  shows an example of virtual staining of UV-AutoM images utilizing a paired training method.  FIG. 15A  is a UV-AutoM image of a deparaffinized FFPE mouse brain section with 7-μm thickness, and  FIG. 15B  is a virtual stained version of the UV-AutoM image utilizing a GAN model in the form of a paired pix2pix based network while  FIG. 15C  is the corresponding bright-field H&amp;E image which served for comparison purposes. Color transformation via paired dataset enabled accurate and reliable generation of a histology-like image. However, any paired training approach requires rigorous data pre-processing procedures on image alignment and registration, and it is difficult to apply on virtual staining of thick samples. Cycle-GAN based networks were found to allow color mapping without paired training examples, and demonstrated great potential on biological tissues with any thickness. The Cycle-GAN network  1100  can be fed with unpaired UV-AutoM and H&amp;E images from a deparaffinized FFPE mouse brain section. The well-trained Cycle-GAN  1100  network enables transformation of a UV-AutoM image of an unlabeled tissue into a virtual H&amp;E-stained version of the unlabeled tissue, thereby allowing easy interpretation of UV-AutoM images for pathologists. 
       FIGS. 16A and 16B  relate to testing results of the trained Cycle-GAN network  1100  on mouse brain samples with different thicknesses.  FIGS. 16A and 16B  are SI reconstructed high resolution UV-AutoM images of mouse brain samples with a thickness of 100 μm and 200 μm, respectively, while  FIGS. 16C and 16D  are corresponding virtually-stained H&amp;E images. Successful color mapping from UV-AutoM contrast to H&amp;E-stained version greatly facilitates UV-AutoM imaging modality to be developed as a practical intraoperative diagnosis tool that can be used by medical doctors and pathologists in an operating room. 
     It will be appreciated that a complementary contrast exits between UV-PAM and UV-AutoM images which thereby enables a method and system of generating both UV-PAM and UV-AutoM images. Photon (or fluorescence) is generated via radiative relaxation while heat is generated via non-radiative relaxation, in which PA wave is released via the heat-induced pressure/temperature rise of the sample. Consequently, PA and autofluorescence images are expected to exhibit a complementary contrast in accordance with the conservation of energy. This contrast is experimetnally demonstrated in  FIGS. 17A and 17B . With UV laser (266 nm) excitation,  FIG. 17A  shows the label-free PA contrast (UV-PAM), whereas  FIG. 17B  shows the label-free autofluorescence contrast (UV-AutoM), of a hippocampus region from a deparaffinized FFPE mouse brain sample with 7-μm thickness.  FIG. 17C  is the corresponding bright-field H&amp;E-stained image, which shows structural similarity with both UV-PAM and UV-AutoM images. Since the strong absorption of nucleus in the UV range, the nuclei concentrated at hippocampus appear bright in UV-PAM image while dark in UV-AutoM image. Such complementary imaging contrast mechanism enable a dual-modality label-free imaging facility to provide more structural and functional information of unprocessed fresh tissue. 
     Throughout this description, brain samples were extracted from Swiss Webster mice and subsequently fixed in 10% neutral-buffer formalin at room temperature for 24 hours. For thin slices (2-8 μm), the samples were processed by FFPE workflow and sectioned by a microtome. The FFPE tissue sections were deparaffinized using xylene and mounted on quartz slides to be imaged by the described UV-PAM and UV-AutoM systems, and followed by H&amp;E staining procedures. For thick slices (20 200 μm), the samples were directly cut by a vibratome with different target thickness. 
     Although the invention has been described with reference to a preferred embodiment, it will be appreciated by those skilled in the art that the invention may be embodied in other forms. 
     The advantageous embodiments and/or further developments of the above disclosure—except for example in cases of clear dependencies or inconsistent alternatives—can be applied individually or also in arbitrary combinations with one another.