Abstract:
Systems, methods and computer program products to annotate axial-view spine images with the desired characteristics of not requiring additional views of the spine or cross-referencing features are provided. In one aspect, the disclosed method does not require external training from a manually-labeled data set while being applicable to different imaging modalities and acquisition protocols. In one aspect, the disclosed method achieves near real-time results using integral kernels when implemented on a Graphics Processing Unit.

Description:
FIELD OF DISCLOSURE 
     The present disclosure relates to labeling radiology images, and more particularly to systems, methods and computer program products to label radiology images using the axial view. 
     BACKGROUND 
     The statements in this section merely provide background information related to the disclosure and may not constitute prior art. 
     The annotations (i.e., labels) of the vertebrae and inter-vertebral discs in axial spine Magnetic Resonance Image (MRI) images (i.e., slices) are essential for further diagnosis of various spine disorders. For instance, in MRI, annotating the axial-view slices facilitates the quantification and level-based reporting of common inter-vertebral disc displacements such as protrusion, extrusion, and bulging. 
     Generating labels in a manual fashion is tedious, subjective, and time-consuming especially because the number of 2D slices in a series can be very high, with up to 100 images per axial series. Furthermore, it can be difficult for radiologists to determine the identity of a particular vertebra in an axial slice of the spine, because adjacent vertebrae have very similar appearances. Even when a particular vertebra has been identified in a particular axial slice, a radiologist must remember its identity and location as they make a diagnosis from multiple axial slices in a series. 
     Current spine labeling algorithms focus on the sagittal view only. However, the quantification and level-based reporting of common inter-vertebral disc displacements such as protrusion, extrusion, and bulging require the radiologist to thoroughly inspect all individual axial slices while visually cross-referencing such axial slices to their corresponding position in the sagittal view. This requires labeling the sagittal view, which has at least two limitations. First, sagittal images are not always available for every patient (i.e., only the axial view may be available) while in other cases the two scans might be acquired at different time points. In such cases, when deformations between sagittal and axial images occur because of patient repositioning, cross-referencing may not be reliable. Therefore, localizing the spinal structures in different views becomes challenging (even for an experienced radiologist). Second, using a sagittal-view labeling along with the cross-reference feature allows to label not all, but only a few slices in the axial series. 
     Most of the current spine labeling algorithms address the above described labeling limitations through intensive external training from a manually-labeled data set. Such training aims to refine the algorithms to learn the shapes, textures and appearances of different spinal structures. This knowledge is then used within a classification or regression algorithm such as support vector machine, random forest regression or graphical models to subsequently label different spinal structures in the test image. Such algorithms work very well on data sets that closely match the training data, but require adjustment/retraining for different data sets or if the imaging modality and/or acquisitions protocol are altered (e.g., an algorithm that is trained and built for Computerized Tomography (CT) images may not perform well on Magnetic Resonance (MR) data). This impedes the use of these algorithms in routine clinical practices, where a particular disorder might be analyzed radiologically using several different imaging modalities/protocols with widely variable imaging parameters. 
     BRIEF SUMMARY 
     In view of the above, there is a need for systems, methods, and computer program products which can annotate axial spine images without the need of additional views of the spine or cross-referencing features and does not require external training from a manually-labeled data set. The above-mentioned needs are addressed by the subject matter described herein. 
     According to one aspect of the present disclosure, a system that allows annotations of the axial slices without the need for: 1) referring to sagittal view annotations, 2) referring to cross-reference features, and 3) requiring external training while being applicable to different imaging modalities and acquisition protocols is provided. 
     According to another aspect of the present disclosure, a method that allows annotations of the axial slices without the need for: 1) referring to sagittal view annotations, 2) referring to cross-reference features, and 3) requiring external training while being applicable to different imaging modalities and acquisition protocols is provided. 
     This summary briefly describes aspects of the subject matter described below in the Detailed Description, and is not intended to be used to limit the scope of the subject matter described in the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The features and technical aspects of the system and method disclosed herein will become apparent in the following Detailed Description set forth below when taken in conjunction with the drawings in which like reference numerals indicate identical or functionally similar elements. 
         FIG. 1  shows a block diagram of an example annotation radiology image system according to one aspect of the present disclosure. 
         FIG. 2  shows a flow diagram illustrating an example method of the classification hierarchy of operating the system of  FIG. 1 , according to one aspect of the present disclosure. 
         FIG. 3  shows a flow diagram illustrating implementing an example method of operating the system of  FIG. 1 , according to one aspect of the present disclosure. 
         FIG. 4  shows a block diagram of an example processor system that can be used to implement the systems and methods described herein according to one aspect of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific examples that may be practiced. These examples are described in sufficient detail to enable one skilled in the art to practice the subject matter, and it is to be understood that other examples may be utilized and that logical, mechanical, electrical and other changes may be made without departing from the scope of the subject matter of this disclosure. The following detailed description is, therefore, provided to describe an exemplary implementation and not to be taken as limiting on the scope of the subject matter described in this disclosure. Certain features from different aspects of the following description may be combined to form yet new aspects of the subject matter discussed below. 
     When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. 
     I. Overview 
     Spinal images may be annotated and/or labeled to assist in analyzing such images and/or diagnosing various spine diseases. However, correctly annotating and/or labeling these images is sometimes difficult because, depending on the image being viewed, the number of visible vertebrae and/or disc vary. This can be especially difficult in the case of axial spine images. To overcome some of the deficiencies encountered with some known manual or automatic annotating and/or labeling methods, aspects of the present disclosure disclosed and described herein enable the standalone, automatic labeling of axial spine images using a comprehensive set of geometric measurements of the human spine as input parameters, (e.g., vertebra height and axial area). In addition, aspects disclosed may also annotate axial spine images without the need for: 1) referring to sagittal view annotations, 2) referring to cross-reference features, and 3) requiring external training while being applicable to different imaging modalities and acquisition protocols. 
     Other aspects, such as those discussed below and others as can be appreciated by one having ordinary skill in the art upon reading the enclosed description, are also possible. 
     II. Example System 
       FIG. 1  depicts an example system  100  for annotating images such, as spinal images, according to one aspect of the present disclosure. System  100  includes a computer  102  and an annotator  104  communicatively coupled to computer  102 . In this example, computer  102  includes a user interface  106  and a data input (e.g., a keyboard, mouse, microphone, etc.)  108  and annotator  104  includes a processor  110  and a database  112 . 
     In certain aspects, user interface  106  displays data such as images (e.g., spinal images, radiology images, etc.) and/or annotated images received from annotator  104 . In certain aspects, user interface  106  receives commands and/or input from a user  114  via data input  108 . In aspects where system  100  is used to annotate spinal images, user interface  106  displays a spinal image(s) and/or annotated spinal image(s) and user  114  provides an initial input identifying, for example, a location of a vertebra on the spinal image(s) and/or provides subsequent input identifying the location of a vertebra based on offline, a priori external training. 
       FIG. 2  illustrates a flow diagram of annotator  104  according to one aspect of the present disclosure. While annotator  104  may generate annotations using any suitable labeling algorithm, in some aspects, annotator  104  generates annotations based on a hierarchy of feature levels, where pixel classifications  202  via non-linear probability product kernels (PPK) are followed by classifications of 2D slices  204 , 3D single vertebra structures  206 , and 3D multiple vertebrae structures  208 . In some aspects, annotator  104  embeds robust geometric priors based on anatomical measurements that are well known to those skilled in the relevant art(s) (e.g., vertebra height and axial area). 
     In certain aspects, efficient pixel-level classifications  202  via integral kernels are used. In one aspect, a non-linear classifier is used, which determines whether the neighborhood of each pixel p matches a target distribution denoted P L . Certain aspects can use offline, external training to set an initial vertebrae pixel location to provide initial classifications. In another aspect, user  114  can select a single point p 0 =(x 0 , y 0 ) within the vertebral region in a single 2D axial slice in the series via data input  108  to provide initial training. Then, the target distribution P L  is learned from a window of size w×h centered at p 0 . 
     In one aspect, neighborhood distributions contain contextual information, which provides much richer inputs to the classifier than individual-pixel intensities. For example, let D j :Ω⊂             2 →         , jε[1 . . . N], be a set of input images, which correspond to the axial slices of a given spine series. Ω is the image domain and N is the number of slices in the series. For each Dε{D j , j=1 . . . N} and each pixel p:(x, y)εΩ, a non-linear kernel based classifier is created using the following:
 
sign(φ)( P   p,W,D   ,P   L )−ρ)  Equation 1:

     where P L , is the a priori learned distribution, ρ is a constant and P p,W,D  is the kernel density estimate (KDE) of the distribution of the data D within window W centered at pixel p=(x, y)εΩ: 
               P     p   ,   W   ,   D       =           Σ     p   ∈   W       ⁢       k   z   D     ⁡     (   p   )              W          ⁢     ∀     z   ∈     Z   .                 
[W] is the number of pixels within W and Z is a finite set of bins encoding the space of image variables. k Z   D  is a Dirac kernel: k Z   D =δ(z−D(p)), where δ(t)=1 if t=0, and 0 elsewhere. In certain aspects, a Gaussian kernel can be used instead of δ. φ is a probability product kernel which measures the degree of similarity between two distributions:
 
φ( P   p,W,D   ,P   L )=Σ zεZ   [P   p,W,D ( z ) P   L ( z )] bγ,γε[0,1]   Equation 2:
 
     In certain aspects, an efficient method to determine kernel density estimates and PPK evaluations for large images and arbitrary window sizes via integral kernels can be computed as integral images as follows: 
     Given an image            , the corresponding integral image           is defined as the sum of all pixel intensities to the left and above the current pixel: ℑ D (x, y)=Σ u≦x Σ v≦y D(u, v). The sum of intensities of all pixels within an arbitrary rectangular can be computed from ℑ D  using only the corners of the rectangle:
 
Σ u=x     1     x     2   Σ v=y     1     y     2               D ( u,v )=ℑ D ( x   1   ,y   1 )+ℑ D ( x   2   ,y   2 )−ℑ D ( x   1   ,y   2 )−ℑ D ( x   2   ,y   1 )  Equation 3:

     where (x 1 , y 1 ) are the coordinates of the upper left corner of the rectangle and (x 2 , y 2 ) are those of the lower right corner. 
     In certain aspects, integral images can be treated as integral kernels to enable the computation of the PPK in Equation 2 as follows: 
     For each slice             a set of separate kernel images defined over Ω:          ,          , . . . ,          , zε         , (with           the Dirac kernel defined earlier) are created. Next, an integral kernel image is computed based on each k z :          (x, y)=Σ u≦x Σ v≦y           (u, v).           can be computed from the integral kernel images using five operations for each p=(x, y)εΩ:
     
       
         
           
             
               
                 
                   
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     where 
                 x   1     =     x   -     w   2         ,       x   2     =     x   +     w   2         ,       y   1     =     y   -     h   2         ,       y   2     =     y   -     h   2         ,         
where with w and h being the width and height of            .

     In certain aspects, 2D slice-level features  204  are derived from the area of pixels classified as vertebra in a given 2D slice and from geometric priors. Vertebra pixels are grouped into sets of 4-connected regions: S i , i=1, 2, . . . . These regions are then filtered, building a set S as follows: S={S i |area(S i &gt;A min  and ∥c i , p 0 ∥&lt;d max }, where A min  and d max  are pre-specified geometric priors, which for example, can be defined so as to reflect human spine measurements that are well known in the clinical literature. If S≠Ø, the area of the largest region in S is used as a 2D slice-level feature for the next step. Otherwise, a value of 0 is assigned to this feature. This feature is denoted as A k  for slice            .
     In certain aspects, 3D single-vertebra classifications  206  are identified. An input set of adjacent slices            , k⊂[1, N], in the neighborhood of a vertebrae are used. These slices are all the slices within a geometric prior height H S , either centered on the initial point or starting at the uppermost (or lowermost) slice of a previously identified vertebra. The slices are then classified (labeled) as either being vertebra or intervertebral disk. The input of this step uses the 2D slice-level feature computed at the previous step (A k ). A one-dimensional smoothing filter is then applied to the features of the adjacent slices A k   2 =A k *K, where A k   s  is the smoothed data and K is a one-dimensional convolution kernel. A slice is classified as vertebra if A k   s &gt;t area , where t area  is a threshold given by t area =c a μ area , with c a  a user defined factor, for example, and μ area  the average of areas A k   s . If the set of adjacent slices classified as vertebrae results in a vertebral height larger than a geometric prior H min , then the 3D set of adjacent slices is classified as vertebra. In certain aspects, the geometric priors H S  and H min  can be defined from well-known anatomical measurements from the literature.
     In certain aspects, an iterative model using 3D multiple vertebrae classification  208  updating is employed to improve classification accuracy. Using the previously found vertebra, the distribution P L  and search region are updated as necessary for finding the next vertebra. The classification or labeling then proceeds in both vertical directions of the spine. For the first vertebra, the initial search height, which is denoted as H S   0  can be defined to be twice the height of a vertebra (which may be obtained from the literature, for example), and centered at the input point, for example. For finding subsequent vertebrae, the search range H S  can begin at the boundary of the previous vertebra and extends for the height of a vertebra plus two inter-vertebral disc spaces (which may also be obtained from the literature), for example. 
     III. Example Method 
     A flowchart representative of example machine readable instructions for implementing the axial image annotation process  300  of the example system  100  is shown in  FIG. 3 . In these examples, the machine readable instructions comprise a program for execution by a processor such as processor  412  shown in the example processor platform  400  discussed below in connection with  FIG. 4 . The program can be embodied in software stored on a tangible computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a BLU-RAY™ disk, or a memory associated with processor  412 , but the entire program and/or parts thereof could alternatively be executed by a device other than processor  412  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowchart illustrated in  FIG. 3 , many other methods of implementing the example annotator can alternatively be used. For example, the order of execution of the blocks can be changed, and/or some of the blocks described can be changed, eliminated, or combined. 
     As mentioned above, process  300  may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a tangible computer readable storage medium such as a hard disk drive, a flash memory, a read-only memory (ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, a random-access memory (RAM) and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term tangible computer readable storage medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, “tangible computer readable storage medium” and “tangible machine readable storage medium” are used interchangeably. 
     Additionally or alternatively, process  300  may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, when the phrase “at least” is used as the transition term in a preamble of a claim, it is open-ended in the same manner as the term “comprising” is open ended. 
     Process  300  begins at block  302  where computer  102  receives, via data input  108 , initial input on a spine image displayed at user interface  106  and/or stored in database  112  (block  302 ). In certain aspects of the present disclosure, to provide the initial training, the initial input is associated with a user  114  clicking on a point p=(x 0 , y 0 ) within the vertebral region in a single 2D axial slice of the spinal image series. In certain aspects, the initial training input is provided by previous, off-line, external training from another data set. 
     At block  304 , annotator  104  generates a target distribution P L  which is learned from a window of size w×h centered at p 0 . 
     At block  306 , annotator  104  sets the initial search height H S   0 , which is defined to be twice the height of an average vertebra (as defined in the clinical literature). 
     For each slice D j  in the set of slices within the search height H S , the annotator  104  performs a pixel classification and a 2D slice classification. At block  308 , the pixel classification is performed by using sign(φ(           P L )−ρ) to classify each pixel p via integral kernels as described above. At block  310 , the slice classification is performed by computing the 2D slice-level feature A j .
     At block  312 , annotator  104  determines if all of the slices within the search height H S  have been completed. If not, the pixel and slice classification is performed on the next slice in the series. 
     At block  314 , once all the slices have been classified at the pixel and slice levels, the vertebra classification is performed by computing the smoothed features A j   s  for each D j  within the search height H S . In addition, at block  316 , the annotator then determines the uppermost and lowermost slices for the current vertebra by using the equation sign (A j   2 −t area ). 
     At block  318 , annotator  104  updates the vertical search height H S  and target distribution P L . 
     At block  320 , annotator  104  determines if all the vertebrae have been completed. If not, at block  322 , annotator  104  determines if the current vertebra being examined, V n , is lower than the top-most vertebra V max . If so, the vertebra is incremented at block  324  (V n =V n+1 ) and the process of classifying the vertebra begins again at block  308 . If the vertebra V n  is already at V max  then the current vertebra being examined is reset at block  326  (V n =V 0 ) and annotator  104  determines if the vertebra being examined is higher than the lower-most vertebra at block  328  (V n &gt;V min ). If so, the vertebra being examined is decremented at block  330  (V n =V n−1 ) and the process of classifying the vertebra continues at block  308 . 
     Once annotator  104  determines that all the vertebra have been completed, annotator  104  assigns labels to the disks. In certain aspects, annotator  104  also labels the intervertebral discs in between the vertebrae. In certain aspects, the annotated spine including vertebrae and disc labels is displayed to user  114  using user interface  106  and/or saved in database  112 . 
     IV. Computing Device 
     The subject matter of this description may be implemented as stand-alone system or for execution as an application capable of execution by one or more computing devices  102 . The application (e.g., webpage, downloadable applet or other mobile executable) can generate the various displays or graphic/visual representations described herein as graphic user interfaces (GUIs) or other visual illustrations, which may be generated as webpages or the like, in a manner to facilitate interfacing (receiving input/instructions, generating graphic illustrations) with users via the computing device(s). 
     Memory and processor  110  as referred to herein can be stand-alone or integrally constructed as part of various programmable devices, including for example a desktop computer, tablet, mobile device or laptop computer hard-drive, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), programmable logic devices (PLDs), etc. or the like or as part of a Computing Device, and any combination thereof operable to execute the instructions associated with implementing the method of the subject matter described herein. 
     Computing device as referenced herein may include: a mobile telephone; a computer such as a desktop or laptop type; a Personal Digital Assistant (PDA) or mobile phone; a notebook, tablet or other mobile computing device; or the like and any combination thereof. 
     Computer readable storage medium or computer program product as referenced herein is tangible (and alternatively as non-transitory, defined above) and may include volatile and non-volatile, removable and non-removable media for storage of electronic-formatted information such as computer readable program instructions or modules of instructions, data, etc. that may be stand-alone or as part of a computing device. Examples of computer readable storage medium or computer program products may include, but are not limited to, RAM, ROM, EEPROM, Flash memory, CD-ROM, DVD-ROM or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired electronic format of information and which can be accessed by the processor or at least a portion of the computing device. 
     The terms module and component as referenced herein generally represent program code or instructions that causes specified tasks when executed on a processor. The program code can be stored in one or more computer readable mediums. 
     Network as referenced herein may include, but is not limited to, a wide area network (WAN); a local area network (LAN); the Internet; wired or wireless (e.g., optical, Bluetooth, radio frequency (RF)) network; a cloud-based computing infrastructure of computers, routers, servers, gateways, etc.; or any combination thereof associated therewith that allows the system or portion thereof to communicate with one or more computing devices. 
     The term user and/or the plural form of this term is used to generally refer to those persons capable of accessing, using, or benefiting from the present disclosure. 
       FIG. 4  is a block diagram of an example processor platform  400  capable of executing process  300  for annotating images such as spinal images. Processor platform  400  may be, for example, a server, a personal computer, a mobile device (e.g., a cell phone, a smart phone, a tablet such as an IPAD™), a personal digital assistant (PDA), an Internet appliance, or any other type of computing device. 
     Processor platform  400  includes a processor  412 . Processor  412  of the illustrated example is hardware. For example, processor  412  may be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. 
     Processor  412  includes a local memory  413  (e.g., a cache). Processor  412  of the illustrated example is in communication with a main memory including a volatile memory  414  and a non-volatile memory  416  via a bus  418 . Volatile memory  414  can be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory  416  can be implemented by flash memory and/or any other desired type of memory device. Access to main memory  414 ,  416  is controlled by a memory controller. 
     Processor platform  400  also includes an interface circuit  420 . Interface circuit  420  can be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface. 
     One or more input devices  422  are connected to the interface circuit  420 . Input device(s)  422  permit(s) a user to enter data and commands into processor  412 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. 
     One or more output devices  424  are also connected to interface circuit  420  of the illustrated example. Output devices  424  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a tactile output device, a light emitting diode (LED), a printer and/or speakers). Interface circuit  420  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor. 
     Interface circuit  420  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  426  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     Processor platform  400  of the illustrated example also includes one or more mass storage devices  428  for storing software and/or data. Examples of such mass storage devices  428  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives. 
     Coded instructions  432  may be stored in mass storage device  428 , in volatile memory  414 , in the non-volatile memory  416 , and/or on a removable tangible computer readable storage medium such as a CD or DVD. 
     VI. Conclusion 
     This written description uses examples to disclose the subject matter, and to enable one skilled in the art to make and use the invention. The above disclosed methods and apparatus disclosed and described herein enable the standalone automatic labeling of axial spine images. From the foregoing, it will be appreciated that the above disclosed methods and apparatus provide a protocol the enables fast and user-friendly visualizations of spine annotations and/or substantially guarantees correct results in substantially all clinical scenarios. The patentable scope of the subject matter is defined by the following claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.