Patent Description:
In some examples, this disclosed technology relates to identifying regions of interest within a digital image, for example, identifying foreground objects from background scenes, or identifying cancer cells within a digital histopathology image. The types of cancer in the cancer cells may include, but are not necessarily limited to, breast cancer, bladder cancer, brain cancer, lung cancer, pancreatic cancer, skin cancer, colorectal cancer, prostate cancer, stomach cancer, liver cancer, cervical cancer, esophageal cancer, leukemia, non-hodgkin lymphoma, kidney cancer, uterine cancer, bile duct cancer, bone cancer, ovarian cancer, gallbladder cancer, gastrointestinal cancer, oral cancer, throat cancer, ocular cancer, pelvic cancer, spinal cancer, testicular cancer, vaginal cancer, vulvar cancer, and thyroid cancer. The regions of interest/classes of interest may also be broader and include abnormal tissue, benign tissue, malignant tissue, bone tissue, skin tissue, nerve tissue, interstitial tissue, muscle tissue, connective tissue, scar tissue, lymphoid tissue, fat, epithelial tissue, nervous tissue, and blood vessels.

The tissue may be collected from a subject in multiple settings including biopsy, surgery, or autopsy. After tissues are removed from the subject, they are prepared for chemical fixation by being placed in a fixative such as formalin to prevent decay of the tissue. The tissues are then either frozen or set in molten wax. Sections of the tissues are then cut and placed on slides.

Once the tissue sections are on slides, a pathologist views the slides through a microscope to determine whether the tissue is, for example, diseased and, if diseased, determine the stage of the disease. For example, a pathologist may determine whether a breast lump includes breast cancer cells and, if it does, a pathologist may determine the grade and/or stage of cancer. Pathologists may also make determinations regarding tissue other than whether it is diseased. For example, a pathologist may determine whether tissue includes lymphocytes. However, there is a technical problem with these determinations in that they are often unreliable, expensive, time consuming, and generally require verification by multiple pathologists to minimize the likelihood of false determinations.

One solution to this technical problem is to use computer vision to determine a tissue characteristic, such as the type and/or grade of cancer by training a neural network (or other machine learning system) to determine whether a digital image of tissue is diseased and determine the type (e.g., breast cancer) and stage (e.g., stage <NUM>) of the disease. However, there is a technical problem with this approach in that, for example, it requires a lot of training data for each disease (e.g., a large amount of positive and negative training patches of various cancers would be required).

<NPL>, describes a patch-based convolutional neural network for whole slide tissue image classification.

<CIT> describes systems and computer-implemented methods for automatic immune cell detection intended to be of assistance in clinical immune profile studies. The automatic immune cell detection method involves retrieving a plurality of image channels from a multi-channel image such as an RGB image or biologically meaningful unmixed image. A cell detector is trained to identify the immune cells by a convolutional neural network in one or multiple image channels. Further, the automatic immune cell detection algorithm involves utilizing a non-maximum suppression algorithm to obtain the immune cell coordinates from a probability map of immune cell presence possibility generated from the convolutional neural network classifier.

Embodiments of the present invention solve the above technical problem and provide a technical solution as defined in appended claims <NUM> and <NUM>. Optional features are defined in appended claims <NUM> to <NUM>.

While the invention is described with reference to the above drawings, the drawings are intended to be illustrative, and the invention is defined by the appended claims.

The present invention, as defined in the appended claims, will now be described more fully hereinafter with reference to the accompanying drawings which show, by way of illustration, specific examples by which the invention may be practiced. Among other things, the present invention may be embodied as devices or methods. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects.

Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrases "in one example," "in an example," and the like, as used herein, does not necessarily refer to the same example, though it may. Furthermore, the phrase "in another example " as used herein does not necessarily refer to a different example, although it may. Thus, as described below, various example of the specification may be readily combined, without departing from the scope of the invention as defined in the appended claims.

In addition, as used herein, the term "or" is an inclusive "or" operator, and is equivalent to the term "and/or," unless the context clearly dictates otherwise. The term "based on" is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of" a," "an," and "the" includes plural references. The meaning of "in" includes "in" and "on.

It is noted that description herein is not intended as an extensive overview, and as such, concepts may be simplified in the interests of clarity and brevity.

Any process described in this application may be performed in any order and may omit any of the steps in the process. Processes may also be combined with other processes or steps of other processes.

<FIG> illustrates components of one example of an environment in which the invention may be practiced. Not all of the components may be required to practice the invention, and variations in the arrangement and type of the components may be made without departing from the scope of the invention as defined in the appended claims. As shown, the system <NUM> includes one or more Local Area Networks ("LANs") / Wide Area Networks ("WANs") <NUM>, one or more wireless networks <NUM>, one or more wired or wireless client devices <NUM>, mobile or other wireless client devices <NUM>-<NUM>, servers <NUM>-<NUM>, optical microscope system <NUM>, laser <NUM>, and may include or communicate with one or more data stores or databases. Various of the client devices <NUM>-<NUM> may include, for example, desktop computers, laptop computers, set top boxes, tablets, cell phones, smart phones, and the like. The servers <NUM>-<NUM> can include, for example, one or more application servers, content servers, search servers, web servers, Graphics Processing Unit (GPU) servers, and the like.

Optical microscope system <NUM> may include a microscope, an ocular assembly, a camera, a slide platform, as well as components of electronic device <NUM> as shown in <FIG>. Although <FIG> shows optical microscope system <NUM> being communicatively coupled to network <NUM>, it may also be coupled to any or all of servers <NUM>-<NUM>, wireless network <NUM>, and/or any of client devices <NUM>-<NUM>.

Laser <NUM>, which may be connected to network <NUM>, may be used for cutting a portion of tissue believed to have cancer cells (or other types of cells).

<FIG> illustrates a block diagram of an electronic device <NUM> that can implement one or more aspects of systems and methods for interactive video generation and rendering according to one example of the specification. Instances of the electronic device <NUM> may include servers, e.g., servers <NUM>-<NUM>, optical microscope system <NUM>, and client devices, e.g., client devices <NUM>-<NUM>. In general, the electronic device <NUM> includes a processor/CPU <NUM>, memory <NUM>, a power supply <NUM>, and input/output (I/O) components/devices <NUM>, e.g., microphones, speakers, displays, smartphone displays, touchscreens, keyboards, mice, keypads, GPS components, etc., which may be operable, for example, to provide graphical user interfaces.

A user may provide input via a touchscreen of an electronic device <NUM>. A touchscreen may determine whether a user is providing input by, for example, determining whether the user is touching the touchscreen with a part of the user's body such as his or her fingers. The electronic device <NUM> can also include a communications bus <NUM> that connects the aforementioned elements of the electronic device <NUM>. Network interfaces <NUM> can include a receiver and a transmitter (or transceiver), and one or more antennas for wireless communications.

The processor <NUM> can include one or more of any type of processing device, e.g., a Central Processing Unit (CPU). Also, for example, the processor can be central processing logic or other logic, may include hardware, firmware, software, or combinations thereof, to perform one or more functions or actions, or to cause one or more functions or actions from one or more other components. Also, based on a desired application or need, central processing logic, or other logic, may include, for example, a software controlled microprocessor, discrete logic, e.g., an Application Specific Integrated Circuit (ASIC), a programmable/programmed logic device, memory device containing instructions, etc., or combinatorial logic embodied in hardware. Furthermore, logic may also be fully embodied as software. Electronic device <NUM> may also include a GPU (not shown), a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation and processing of images in a frame buffer intended for output to a display device.

The memory <NUM>, which can include Random Access Memory (RAM) <NUM> and Read Only Memory (ROM) <NUM>, can be enabled by one or more of any type of memory device, e.g., a primary (directly accessible by the CPU) or secondary (indirectly accessible by the CPU) storage device (e.g., flash memory, magnetic disk, optical disk, and the like). The RAM can include an operating system <NUM>, data storage <NUM>, which may include one or more databases, and programs and/or applications <NUM>, which can include, for example, software aspects of the digital histopathology and microdissection program <NUM>. The ROM <NUM> can also include Basic Input / Output System (BIOS) <NUM> of the electronic device.

The program <NUM> is intended to broadly include or represent all programming, applications, algorithms, software and other tools necessary to implement or facilitate methods and systems according to examples of the specification. The elements of systems and methods for interactive video generation and rendering program may exist on a single server computer or be distributed among multiple computers, servers, devices or entities, which can include advertisers, publishers, data providers, etc. If the systems and methods for interactive video generation and rendering program is distributed among multiple computers, servers, devices or entities, such multiple computers would communicate, for example, as shown on <FIG>.

The power supply <NUM> contains one or more power components, and facilitates supply and management of power to the electronic device <NUM>.

The input/output components, including Input / Output (I/O) interfaces <NUM>, can include, for example, any interfaces for facilitating communication between any components of the electronic device <NUM>, components of external devices (e.g., components of other devices of the network or system <NUM>), and end users. For example, such components can include a network card that may be an integration of a receiver, a transmitter, a transceiver, and one or more input/output interfaces. A network card, for example, can facilitate wired or wireless communication with other devices of a network. In cases of wireless communication, an antenna can facilitate such communication. Also, some of the input/output interfaces <NUM> and the bus <NUM> can facilitate communication between components of the electronic device <NUM>, and in an example can ease processing performed by the processor <NUM>.

Where the electronic device <NUM> is a server, it can include a computing device that can be capable of sending or receiving signals, e.g., via a wired or wireless network, or may be capable of processing or storing signals, e.g., in memory as physical memory states. The server may be an application server that includes a configuration to provide one or more applications, e.g., aspects of the systems and methods for interactive video generation and rendering, via a network to another device. Also, an application server may, for example, host a Web site that can provide a user interface for administration of example aspects of the systems and methods for interactive video generation and rendering.

Any computing device capable of sending, receiving, and processing data over a wired and/or a wireless network may act as a server, such as in facilitating aspects of implementations of the systems and methods for interactive video generation and rendering. Thus, devices acting as a server may include devices such as dedicated rack-mounted servers, desktop computers, laptop computers, set top boxes, integrated devices combining one or more of the preceding devices, and the like.

Servers may vary widely in configuration and capabilities, but they generally include one or more central processing units, memory, mass data storage, a power supply, wired or wireless network interfaces, input/output interfaces, and an operating system such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and the like.

A server may include, for example, a device that is configured, or includes a configuration, to provide data or content via one or more networks to another device, such as in facilitating aspects of an example systems and methods for interactive video generation and rendering. One or more servers may, for example, be used in hosting a Web site, such as the web site www. One or more servers may host a variety of sites, such as, for example, business sites, informational sites, social networking sites, educational sites, wikis, financial sites, government sites, personal sites, and the like.

Servers may also, for example, provide a variety of services, such as Web services, third-party services, audio services, video services, email services, HTTP or HTTPS services, Instant Messaging (IM) services, Short Message Service (SMS) services, Multimedia Messaging Service (MMS) services, File Transfer Protocol (FTP) services, Voice Over IP (VOIP) services, calendaring services, phone services, and the like, all of which may work in conjunction with example aspects of an example systems and methods for interactive video generation and rendering. Content may include, for example, text, images, audio, video, and the like.

In example aspects of the systems and methods for interactive video generation and rendering, client devices may include, for example, any computing device capable of sending and receiving data over a wired and/or a wireless network. Such client devices may include desktop computers as well as portable devices such as cellular telephones, smart phones, display pagers, Radio Frequency (RF) devices, Infrared (IR) devices, Personal Digital Assistants (PDAs), handheld computers, GPS-enabled devices tablet computers, sensor-equipped devices, laptop computers, set top boxes, wearable computers, integrated devices combining one or more of the preceding devices, and the like.

Client devices, as may be used in example systems and methods for interactive video generation and rendering, may range widely in terms of capabilities and features. For example, a cell phone, smart phone or tablet may have a numeric keypad and a few lines of monochrome Liquid-Crystal Display (LCD) display on which only text may be displayed. In another example, a Web-enabled client device may have a physical or virtual keyboard, data storage (such as flash memory or SD cards), accelerometers, gyroscopes, GPS or other location-aware capability, and a 2D or 3D touch-sensitive color screen on which both text and graphics may be displayed.

Client devices, such as client devices <NUM>-<NUM>, for example, as may be used in example systems and methods for interactive video generation and rendering, may run a variety of operating systems, including personal computer operating systems such as Windows, iOS or Linux, and mobile operating systems such as iOS, Android, Windows Mobile, and the like. Client devices may be used to run one or more applications that are configured to send or receive data from another computing device. Client applications may provide and receive textual content, multimedia information, and the like. Client applications may perform actions such as browsing webpages, using a web search engine, interacting with various apps stored on a smart phone, sending and receiving messages via email, SMS, or MMS, playing games (such as fantasy sports leagues), receiving advertising, watching locally stored or streamed video, or participating in social networks.

In example aspects of the systems and methods for interactive video generation and rendering, one or more networks, such as networks <NUM> or <NUM>, for example, may couple servers and client devices with other computing devices, including through wireless network to client devices. A network may be enabled to employ any form of computer readable media for communicating information from one electronic device to another. A network may include the Internet in addition to Local Area Networks (LANs), Wide Area Networks (WANs), direct connections, such as through a Universal Serial Bus (USB) port, other forms of computer-readable media, or any combination thereof. On an interconnected set of LANs, including those based on differing architectures and protocols, a router acts as a link between LANs, enabling data to be sent from one to another.

Communication links within LANs may include twisted wire pair or coaxial cable, while communication links between networks may utilize analog telephone lines, cable lines, optical lines, full or fractional dedicated digital lines including T1, T2, T3, and T4, Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links including satellite links, optic fiber links, or other communications links known to those skilled in the art. Furthermore, remote computers and other related electronic devices could be remotely connected to either LANs or WANs via a modem and a telephone link.

A wireless network, such as wireless network <NUM>, as in example systems and methods for interactive video generation and rendering, may couple devices with a network. A wireless network may employ stand-alone ad-hoc networks, mesh networks, Wireless LAN (WLAN) networks, cellular networks, and the like.

A wireless network may further include an autonomous system of terminals, gateways, routers, or the like connected by wireless radio links, or the like. These connectors may be configured to move freely and randomly and organize themselves arbitrarily, such that the topology of wireless network may change rapidly. A wireless network may further employ a plurality of access technologies including 2nd (<NUM>), 3rd (<NUM>), 4th (<NUM>) generation, Long Term Evolution (LTE) radio access for cellular systems, WLAN, Wireless Router (WR) mesh, and the like. Access technologies such as <NUM>, <NUM>, <NUM>, <NUM>, and future access networks may enable wide area coverage for client devices, such as client devices with various degrees of mobility. For example, a wireless network may enable a radio connection through a radio network access technology such as Global System for Mobile communication (GSM), Universal Mobile Telecommunications System (UMTS), General Packet Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced, Wideband Code Division Multiple Access (WCDMA), Bluetooth, <NUM>. 11b/g/n, and the like. A wireless network may include virtually any wireless communication mechanism by which information may travel between client devices and another computing device, network, and the like.

Internet Protocol (IP) may be used for transmitting data communication packets over a network of participating digital communication networks, and may include protocols such as TCP/IP, UDP, DECnet, NetBEUI, IPX, Appletalk, and the like. Versions of the Internet Protocol include IPv4 and IPv6. The Internet includes local area networks (LANs), Wide Area Networks (WANs), wireless networks, and long haul public networks that may allow packets to be communicated between the local area networks. The packets may be transmitted between nodes in the network to sites each of which has a unique local network address. A data communication packet may be sent through the Internet from a user site via an access node connected to the Internet. The packet may be forwarded through the network nodes to any target site connected to the network provided that the site address of the target site is included in a header of the packet. Each packet communicated over the Internet may be routed via a path determined by gateways and servers that switch the packet according to the target address and the availability of a network path to connect to the target site.

The header of the packet may include, for example, the source port (<NUM> bits), destination port (<NUM> bits), sequence number (<NUM> bits), acknowledgement number (<NUM> bits), data offset (<NUM> bits), reserved (<NUM> bits), checksum (<NUM> bits), urgent pointer (<NUM> bits), options (variable number of bits in multiple of <NUM> bits in length), padding (may be composed of all zeros and includes a number of bits such that the header ends on a <NUM> bit boundary). The number of bits for each of the above may also be higher or lower.

A "content delivery network" or "content distribution network" (CDN), as may be used in example systems and methods for interactive video generation and rendering, generally refers to a distributed computer system that comprises a collection of autonomous computers linked by a network or networks, together with the software, systems, protocols and techniques designed to facilitate various services, such as the storage, caching, or transmission of content, streaming media and applications on behalf of content providers. Such services may make use of ancillary technologies including, but not limited to, "cloud computing," distributed storage, DNS request handling, provisioning, data monitoring and reporting, content targeting, personalization, and business intelligence. A CDN may also enable an entity to operate and/or manage a third party's Web site infrastructure, in whole or in part, on the third party's behalf.

A Peer-to-Peer (or P2P) computer network relies primarily on the computing power and bandwidth of the participants in the network rather than concentrating it in a given set of dedicated servers. P2P networks are typically used for connecting nodes via largely ad hoc connections. A pure peer-to-peer network does not have a notion of clients or servers, but only equal peer nodes that simultaneously function as both "clients" and "servers" to the other nodes on the network.

One example of the present specification includes systems, methods, and a non-transitory computer readable storage medium or media tangibly storing computer program logic capable of being executed by a computer processor, related to digital histopathology and microdissection.

As mentioned above, methods that rely entirely on various pathologists to review and make determinations as to whether a tissue sample ("sample") has a certain characteristic such as being diseased or, in particular, diseased with cancer can be unreliable, expensive, or time consuming. On the other hand, if the determination is made solely by a neural network, a large amount of training data may be necessary for each of the various tissue characteristics such as types and grades of diseases (including both positive and negative training data), which is difficult to collect. For example, generating such training data may require receiving input from one or more pathologists for a large number of images as to whether such images are positive or negative for a particular disease or other characteristic of an image of a sample.

An example of the present specification includes determining whether a sample is diseased. The example described below refers, in particular, to cancer. However, examples of the present specification may be used to make a determination as to other characteristics of a sample. For example, examples of the present specification may be used to determine whether a sample shows other diseases or grades of diseases. As another example, examples of the present specification may be used to determine whether a sample includes lymphocytes.

An example of the present specification relates to determining whether a sample is cancerous by using computer vision and input from one or more pathologists as to whether one or more patches of an image are positive or negative for a particular type or grade of cancer. Other examples relate to recognizing whether a sample (e.g., image, sound, pattern, etc.) is positive or negative for satisfying a criteria (e.g., criteria for recognizing a person, object, sound, pattern, etc.).

Computer vision relates to the automated extraction, analysis and understanding of useful information from one or more digital images. For example, computer vision may be used to determine the age of a person in a photograph by determining the location of a face of the person in a digital image, determining the location of the eyes of such person, and measuring the interpupillary distance of such person.

In the field of machine learning, a Convolutional Neural Network ("CNN") is an artificial neural network which may be used in the field of computer vision. The article Rethinking the Inception Architecture for Computer Vision by <NPL>) discusses the use of CNNs in computer vision.

<FIG> illustrates an architecture diagram of an electronic device that can implement one or more aspects of an example of the specification. <FIG> includes image processing engine <NUM> which processes digital images to output regions of interest <NUM>. Image processing engine <NUM> includes tile generation engine <NUM>, feature extraction engine <NUM>, user selection engine <NUM>, SVM Training Engine <NUM>, classification engine <NUM>, and grouping engine <NUM>. Image processing engine <NUM> is coupled to (e.g., in communication with) CNN <NUM>, digital tissue images database <NUM>, training patches databases 302F and 302J, positive one-class Support Vector Machine (SVM) <NUM>, negative one-class SVM <NUM>, and two-class SVM <NUM>. Image processing engine <NUM> is also coupled to CNN <NUM>, which can utilize the output of image processing engine <NUM> as training data to train CNN <NUM> to classify other digital images.

<FIG> shows a deep learning architecture of a CNN. The CNN has a plurality of layers, as shown in <FIG>, and a plurality of parameters in each layer (input size). <FIG> includes information on the type of layer, the patch size, and the input size of each layer. The values of the parameters determine the output of the CNN.

The CNN <NUM> may be provided an input of an image of a tissue sample (or a patch of such an image) and the CNN <NUM> may provide, as an output, a plurality of (for example, <NUM>) image feature values (i.e., feature extraction or feature representation of visual descriptors). Such output would be from the linear layer. The softmax layer, shown directly below the linear layer in <FIG>, may not need to be used and may be removed from the architecture of CNN <NUM>. The image of the tissue sample may be a slide image and, in particular, a digital histopathology image.

<FIG> illustrates a process carried out by an electronic device that can implement one or more aspects of an example of the specification. More specifically, <FIG> shows an end-to-end high level process beginning with a histology technician cutting a tissue sample into slices and ending with the technician cutting a portion of the sample determined by the system to be cancerous (or another disease) from the sample. Reference will be made to the hardware architecture diagram shown in <FIG>. It should be understood that <FIG> omits certain hardware for simplicity. For example, <FIG> does not show networks, switches, routers, and the like. However, one of ordinary skill in the art would understand that clients are connected to servers or other clients via networks and servers are connected to servers via networks. That is, for example, clients <NUM> and <NUM> of <FIG> may be any of client devices <NUM>-<NUM>, optical microscope system <NUM>, or laser <NUM> of <FIG>, and servers <NUM>, <NUM>, <NUM>, and <NUM> of <FIG> may be servers <NUM>-<NUM> of <FIG>. In the alternative, any of the processing described below may take place on any other device (for example, processing that takes place on a web server may instead take place on a GPU server).

In step 500A of <FIG>, a histology technician receives a block tissue sample and cuts the tissue sample into thin and thick slices. The thin slices are used to scan and the thick slices are used for microdissection once the relevant portion of a thin slice is identified. The technician may be located, for example, at a hospital, doctor's office, lab, or the like.

In step 500B, the technician stains the thin slice with, for example, Hematoxylin and Eosin (H&E) and scans the H&E glass slide to generate a digital tissue image, also called a Whole Slide Image (WSI). The scanning may use a <NUM>,000x200,<NUM> resolution scanner. A single WSI may be approximately <NUM> GB in size.

In step 500C, using client <NUM> or client <NUM>, the technician uploads the WSI to web server <NUM> (there may be multiple web servers but only one is shown for simplicity). Web server <NUM> then transmits the WSI to file server <NUM>, which stores a plurality of WSIs and related metadata. Web server <NUM> then uses a queueing engine running on the web server to determine which of GPU Servers <NUM> or <NUM> to use to break up the WSI into patches and extra features based on a load balancing algorithm. Once a GPU server is selected, the web server <NUM> transmits instructions to the selected GPU server (such as GPU server <NUM>) to break up the WSI into patches and extract features using a convolutional neural network running on GPU server <NUM> (as discussed in more detail below with reference to step <NUM> of <FIG>).

In step 500D, once GPU server <NUM> (or <NUM>) extracts the features using the convolutional neural network, it transmits a message to file server <NUM> to store metadata associated with WSI, the metadata including patch location, patch size (e.g., 400x400 pixels), and values of each of the features of the patch extracted by the GPU server <NUM>. The metadata is then stored on file server <NUM> (associated with the stored WSI) and can be accessed by any GPU server (for example, <NUM> and <NUM>) or web server <NUM>.

In step 500E, once the metadata is generated and stored on file server <NUM> for all patches of the WSI, a notification is sent to a user such as a pathologist, by web server <NUM>, that metadata for all patches of a WSI has been generated. The pathologist may be a user at, for example, client <NUM>.

In step 500F, the user at client <NUM> selects one or more positive patches and zero or more negative patches, as discussed in more detail below with reference to step <NUM> of <FIG>. The user's selections are then transmitted from client <NUM> to web server <NUM>, which in turn transmits them to file server <NUM> for storage as metadata associated with the WSI.

In step <NUM>, based on the user's selections and the extracted features, the web server generates a mask such as a microdissection mask as discussed in detail below with regard to steps <NUM>, <NUM>, and <NUM> of <FIG>.

In step <NUM>, the web server <NUM> transmits the mask to the technician at client <NUM>. Then, in step 500J, the technician cuts the portion of the thick slide tissue sample identified in the mask by using laser <NUM>.

<FIG> illustrates a process carried out by an electronic device that can implement one or more aspects of an example of the specification. More specifically, <FIG> shows a method for receiving a digital tissue image of a biological sample and determining the portions thereof likely to have cancer based, in part, on user input as well as machine learning. Once the portions are determined, as discussed with reference to <FIG> above, a portion of a biological sample may be microdissected for further processing. The steps of <FIG> may be performed in the order shown and may also be performed in another order. For example, step <NUM> may be performed after step <NUM> or step <NUM>.

Prior to the CNN <NUM> receiving as an input the relevant tissue sample image (i.e., the test sample) (or patches of such an image) and providing as an output the image feature values, according to an example of the present specification, the CNN <NUM> may be trained on generic images (i.e., not images of cancer cells and images without cancer cells). That is, CNN <NUM> may be considered a trained generic classifier. For example, the CNN <NUM> may be trained using ImageNet images. ImageNet is a large visual database for use in visual object recognition software research, which is organized according to the so-called WordNet hierarchy (which currently includes a large list of nouns), in which each node of the hierarchy includes a large number of images (e.g., <NUM>). See, e.g., http://www. For example, ImageNet includes a large number of images for each of the following nouns, organized in a hierarchy: animal, vertebrae, mammal, placental, primate, monkey, baboon, mandrill. Each category may include synonyms and/or related terms such as animal, which includes related terms animate being, beast, and creature. The large number of images for each term in WordNet (i.e., each class of images) show the relevant subject in various poses, times, ages, locations, image qualities, colors, with additional objects, backgrounds, and the like. There may be, for example, <NUM> nodes in the hierarchy. The plurality of images used to originally train CNN <NUM> may, for example, include a plurality of images (or patches) of various generic images not necessarily related to any diseases. In one example, CNN <NUM> has been previously trained on hundreds of thousands or even millions of such images.

CNN <NUM>, positive one-class SVM <NUM>, negative one-class SVM <NUM>, two-class SVM <NUM>, one-class SVM training patches 302F, two-class SVM training patches 302J, test patches <NUM>, and regions of interest <NUM> may also be stored and executed on servers <NUM>-<NUM> or may instead be stored and executed on one or more client devices <NUM>-<NUM> or a combination of servers <NUM>-<NUM> and/or client devices <NUM>-<NUM>. More specifically, SVMs <NUM>, <NUM> and <NUM> may be stored and executed on web server <NUM> of <FIG>.

Trained CNN <NUM> may be used to extract features from patches of images of biological samples for which the classification is unknown (i.e., test patches). Once the CNN <NUM> is trained, it is ready for use with "test" patches. Test patches, such as test patches <NUM>, are patches from an actual patient's tissue sample that may have been captured using the microscope, ocular assembly, camera, and slide platform of optical microscope system <NUM>.

In step <NUM>, the image processing engine <NUM> obtains access to a digital tissue image of a biological sample. The user may be provided instructions regarding selecting a digital tissue image of a biological sample (i.e., a whole slide image), as shown on <FIG>. The digital image may in various forms, for example, SVS, TIFF, VMS, VMU, NDPI, SCN, MRXS, SVSLIDE, BIF, PDF, JPG, BMP, GIF and any other digital image format. Moreover, the digital image may be located on a server, it may be a large image (many GB in size), the image may be stored in the cloud and all analysis in <FIG> may be performed in the cloud. The cloud may include servers <NUM>-<NUM>. However, the steps of <FIG> may also be performed at one or more client devices <NUM>-<NUM> or a combination of servers <NUM>-<NUM> and/or client devices <NUM>-<NUM>. The processing may be parallel and take place on multiple servers. The digital image may include biological sample metadata including digital information associated with at least one of the following: a tissue type, a tissue donor, a scanner, a stain, a staining technique, an identifier of a preparer, an image size, a sample identifier, a tracking identifier, a version number, a file type, an image date, a symptom, a diagnosis, an identifying information of treating physician, a medical history of the tissue donor, a demographic information of the tissue donor, a medical history of family of the tissue donor, and a species of the tissue donor.

In step <NUM>, tile generation engine <NUM> tiles the digital tissue image into a collection of image patches <NUM> (test patches <NUM>). Each tile/patch in the test patches <NUM> may be, for example, less than or equal to 1000x1000 pixels, less than or equal to 400x400 pixels, less than or equal to 256x256 pixels or any other suitable number of pixels. The tiling step may be performed iteratively or in parallel by one or more computers. Tiling may include creating image patches that are of a uniform size and a uniform shape. The size of the patch may be a function of the size of previous positive patches 302D and previous negative patches 302E, patches previously selected by pathologists as being positive or negative for cancer, grade of cancer, the particular type or grade of suspected cancer of test patches <NUM>, or some other disease. For example, if patches previously selected by pathologists as being positive or negative were 400x400 patches, the tile generation engine <NUM> may tile the image into same size (400x400) patches or, within <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, or <NUM>% of the size of the previous patches.

In step <NUM>, the test patches <NUM> may or may not be of a uniform size and shape. For example, one patch may be 400x400 and another patch may be 300x300 or 300x200. The patches also need not be squares, they may be rectangles, circles, ovals or more complex shapes. Various processes may be used for tiling such as Penrose tiling, bulk exclusion, and/or bound boxes.

In step <NUM>, the generated patches may be overlapping or non-overlapping. That is, the same area of the digital image may or may not be included in more than one tile/patch.

The generated patches are stored as test patches <NUM> in a memory of servers <NUM>-<NUM> and/or client devices <NUM>-<NUM> or a combination of servers <NUM>-<NUM> and/or client devices <NUM>-<NUM>.

In step <NUM>, the feature extraction engine <NUM> extracts a plurality of features from each patch of test patches <NUM> and stores them in test patches <NUM> as metadata or a separate data structure or, outside of test patches <NUM> in a separate database/data structure. In particular, the feature extraction engine <NUM> extracts image features from patches <NUM>. In one example, features extraction engine <NUM> extracts features identified as useful byCNN <NUM>, for example, one based on Inception v3 and trained on a variety of diverse images such as, for example, the ImageNet images. The feature extraction engine <NUM> makes use of the fully trained CNN <NUM> to extract features from images. In one example, feature extraction engine <NUM> communicates with trained CNN <NUM> and provides it, either one at a time or in a single call/message, the patches <NUM> for feature extraction. As CNN <NUM> receives each patch from feature extraction engine <NUM>, processes each of the patches <NUM> through the layers shown in <FIG> beginning with the topmost convolutional layer to the second from the bottom linear layer. The linear layer provides, as output, a number of features (e.g., <NUM>) representing the input patch and provides the <NUM> feature values to feature extraction engine <NUM>, as shown in <FIG>. These values may be provided to feature extraction engine <NUM> by CNN <NUM> in, for example, an array, vector, linked list, or other suitable data structure. The features may then be stored in test patches <NUM>.

In step <NUM>, the feature extraction engine <NUM> may identify/select patches from test patches <NUM> as a function of pixel content, instead of extracting features from all test patches <NUM>. For example, identification may include filtering the patches based on color channels of the pixels within the image patches. For example, the identification may be made as a function of the variance of the patches. The variance of the patches may be based on the variance of the Red Green Blue (RGB) channels and/or Hue, Saturation, Value (HSV) and/or Hue Saturation and/or Luminosity (HLS) and/or Hue Saturation Intensity (HIS) in a particular patch. This step helps insure that only patches that include cells are considered. Once step <NUM> is complete, only patches with cells may be identified/selected for feature extraction.

In step <NUM>, the user selection engine <NUM> determines a user selection of a user selected subset of the collection of image patches. As illustrated in <FIG>, a user may be provided with instructions regarding making the user selection. That is, a Graphical User Interface (GUI) controlled by the user selection engine <NUM> shows a user (such as a pathologist or one or more pathologists at one or more sites) the test patches <NUM>. For example, <FIG> show a plurality of patches that may be shown to a user. A user may be shown a GUI by user selection engine <NUM> similar to <FIG>, <FIG>, or <FIG> (more or less patches may be shown) on an I/O Interface <NUM> such a screen, smartphone screen, tablet, touchscreen, or the like. <FIG> and <FIG> show a GUI in a state after a user has selected one positive patch. A user may select the positive/negative dropdown to indicate whether he or she is selecting positive or negative patches. Once the user selects positive or negative, he or she may click on one or more patches to select them. If the user believes he or she may have made an error in the selection, he or she may click the clear button and begin selecting patches anew. If the "fill hole" checkbox is checked as shown in <FIG>, then, when there is a hole (an area of one or more patches not selected by the user) within an area selected by the user as being positive for cancer, the hole (unselected area) will nevertheless be treated as if it was selected by the user as being positive for cancer and accordingly filled in (the same would be true if the user was selecting negative patches). Once a user is sure he or she has selected all of the positive or negative patches he or she desires, he or she may click the commit button.

First, the user may select whether to select positive patches (i.e., patches the user believes to be positive for cancer, a particular type or grade of cancer, or other relevant disease) or negative patches (i.e., patches the user believes to be negative for cancer, a particular type or grade of cancer, or other relevant disease). Once that selection has been made, the user will select one or more positive or negative patches (depending on whether the user selected negative or positive patches above). Optionally, the user may select positive patches first or may only select positive patches and not negative patches. Once the user is satisfied that he or she has selected an adequate number of patches, he may select the "commit" (or similar) button to let user selection engine <NUM> know that he or she finished selecting a particular set (positive or negative) of patches.

Any positive patches selected by the user are then stored in SVM training patches 302F and, in particular, current positive patches 302B and any negative patches selected by the user are stored in current negative patches 302C. The entire selected patch may be stored or an identifier of the patch in test patches <NUM> may be referenced (for example, if there are <NUM>,<NUM> test patches <NUM> and each has an identifier between <NUM> and <NUM>,<NUM>, current positive patches 302B and current negative patches 302C may store a plurality of identifiers that each reference a test patch in test patches <NUM>. For example, following the user selection in step <NUM>, user selection engine <NUM> may store a linked list in current positive patches 302B with identifiers <NUM>, <NUM>, <NUM>, <NUM> and <NUM>. Similarly, the user selection engine <NUM> may store a linked list in current negative patches 302C with identifiers <NUM>, <NUM>, <NUM>, <NUM> and <NUM>.

The user's selection helps image processing engine <NUM> correctly determine other test patches <NUM> that are either likely negative or likely positive for cancer. Assuming the user correctly selects positive and negative patches, the image processing engine <NUM> may then compare patches it knows to be positive (via user selection of the positive patches in step <NUM>) with other patches in test patches <NUM> and select such patches as likely positive based on feature distances between a candidate test patch in test patches <NUM> and the user selected positive patches, as discussed in more detail below with respect to positive one-class Support Vector Machine (SVM) <NUM>, negative one-class SVM <NUM>, and two-class SVM <NUM>. The image processing engine may similarly compare user selected negative patches and test patches <NUM> to determine likely negative patches.

Although it is possible for image processing engine <NUM> to select likely positive or likely negative patches using CNN <NUM> trained with generic images in step <NUM> and without SVMs <NUM>, <NUM> and <NUM>, as discussed in detail below, such a process, as shown in <FIG>, may have classification errors, as discussed in more detail below.

<FIG> shows two classes of images: (<NUM>) pines and bamboo ("bamboo"); and (<NUM>) dumplings. The images in <FIG> are input into a CNN, for example, Inception v3, which has been trained on generic images such as ImageNet. Based on the ImageNet training, the linear layer of the CNN will generate <NUM> features for each of the test images shown in <FIG>. Image processing engine <NUM> will then calculate the distance (such as the Euclidian/L2 distance of the <NUM> features extracted by the linear layer of the CNN) between each image and each other image (i.e., distance between image pairs). An analysis may be performed to determine which of the <NUM> features are particularly important and increase their weight to generate a modified L2 distance weighing certain features more heavily than others. Using a priori distance thresholds, the system would then classify pairs of images as being in the same class. Once a user makes a selection as to a positive image/patch, the system would then find matching patches using the above analysis. The same analysis can be performed for a negative patch selected by a user.

<FIG> illustrates a vertical bar graph showing the results of the classification of the images in <FIG>, as discussed above. The x-axis represents the difference in L2 distances of pairs (of values of the <NUM> features extracted by the CNN <NUM>). The y-axis is a normalized histogram showing the normalized number of values of pairs that fall into that L2 distance.

According to <FIG>, each green bar (i.e., the first <NUM> bars from the left) shows a matched pair (i.e., bamboo-bamboo or dumpling-dumpling), whereas each red bar (the final <NUM> bars near the right end) shows an unmatched pair (i.e., bamboo-dumpling). As suggested by <FIG>, it may be possible for a user to a priori determine the L2 distance threshold to programmatically separate matched pairs from unmatched pairs based on their L2 distance. For example, if an L2 distance of approximately <NUM> was chosen (where any pair of images for which the L2 distance was less than or equal to <NUM> was considered matched and any pair of images for which the L2 distance was more than <NUM> was considered unmatched), this may appear to provide accurate results of matched pairs and unmatched pairs.

However, as can be seen in <FIG>, the above approach may not work well for other sets of images. <FIG>, similarly to <FIG>, shows two classes of images: (<NUM>) bamboo; and (<NUM>) dumplings. Similarly to <FIG>, <FIG> illustrates a vertical bar graph showing the results of the classification of the images in <FIG>. As can be seen in <FIG>, however, using the same process as in <FIG>, several mismatched pairs (red bars) have lower L2 distances than matched pairs (green bars). Thus, it may not be possible to a priori select an L2 distance threshold that would include only matched pairs with an L2 distance below or equal to the threshold and only unmatched pairs with an L2 distance above the threshold.

And in pathology samples, the variation in test images/patches (of, for example, color and intensity) makes classification even more difficult than in <FIG>. Thus, using the matching method of <FIG> with pathology samples may lead to even more incorrectly matched pairs of images/patches.

Referring back to <FIG>, once a user selection of positive and/or negative patches is determined in step <NUM>, the two-class SVM <NUM> training images are determined in step <NUM>. Step <NUM> is described in further detail with reference to in <FIG> below.

In step 509A, the SVM training engine <NUM> accesses the current positive patches 302B (i.e., the positive patches selected by the user from the current test patches <NUM>) (in general, data such as current positive patches 302B may be accessed directly or may be provided by the previous or other engine, in this case, user selection engine <NUM> to the current engine, SVM training engine <NUM>). In step 509B, SVM training engine <NUM> then accesses the previous positive patches 302D. For example, SVM training engine <NUM> may select all or a predetermined number (such as <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM>) of the most recently selected previous positive patches 302D or it may select all or a predetermined number of previous positive patches 302D that were selected from previous test patches involving the same type of cancer/disease as suspected in test patches <NUM> or it may select all or a predetermined number of previous positive patches 302D that were selected by the current user.

There is a high level of probability that the current positive patches 302B are accurate since a user selected them. However, previous positive patches 302D include patches from other images/patches (not current test patches <NUM>). Thus, it is necessary to determine whether they are relevant to test patches <NUM> by only selecting relevant previous positive patches 302D as compared to current positive patches 302B. Such determination takes place in step 509C-509D.

In step 509C, the SVM training engine <NUM> accesses and trains the positive one-class SVM <NUM> as shown in <FIG>. An SVM is a supervised learning model with associated learning algorithms that analyzes data used for classification and regression analysis (in our case, classification). An SVM first receives as input training data (e.g. images) of one or more classes. It then builds a model based on the training data that is able to determine the class in which a new test image belongs. A one-class SVM has a single class of images that it classifies.

<FIG> illustrates one example of the results of using a one-class SVM with Radial Basis Function (RBF) kernel, according to one or more aspects of an example of the present specification. As can be seen, <FIG> shows multiple bands including <NUM>, <NUM>, and <NUM>. <FIG> also shows green training observations <NUM>, which are the patches used to train the positive one-class SVM <NUM>. The training observations include only the current positive patches 302B (or a predetermined number of current positive patches 302B).

Once the current positive patches 302B are input into the positive one-class SVM <NUM> to train the positive one-class SVM <NUM>, the SVM generates bands using a Gaussian distribution based on the data points (vectors) in the <NUM>-dimension feature space used in a present example to describe the current positive patches 302B. Then, once test patches are input into the positive one-class SVM <NUM>, the test patches <NUM> within the two innermost pink ellipse-like regions <NUM> are most similar to the current positive patches 302B. The innermost, darkest blue band <NUM> that fully encloses the two pink regions <NUM>, would include patches that are less similar than the patches in the innermost pink regions (or, generally, the training patches (current positive patches 302B)). Region <NUM> is even further from the pink regions <NUM> and patches in those regions would be even less similar than patches in pink regions <NUM>.

SVM training engine <NUM> determines which bands signify inclusion in the class of the training data (in this case, positive patches) and which bands signify exclusion. For example, SVM training engine <NUM> may determine (or it may be predetermined) that only previous positive patches 302D (observations) determined to be in regions <NUM> and <NUM> signify inclusion in the same class as the training data (in this example, current positive patches 302B). That is, previous positive patches 302D that positive one-class SVM <NUM> determines are either in regions <NUM> or <NUM> would be considered to be in the same class as current positive patches 302B. That is, following training, SVM training engine <NUM> would provide as an input to positive one-class SVM <NUM> a subset (or all of) of previous positive patches 302D. The positive one-class SVM <NUM> would determine the region of each patch based on the training data and only determine the previous positive patches 302D that are in regions <NUM> or <NUM> to be in the same class.

It is noted that the above process of determining which of the previous positive patches 302D are in the same class as current positive patches 302B via the positive one-class SVM <NUM> in step 509C may be performed by other means of outlier detection. For example, elliptic envelope, or isolation forest (which, for example, detects data-anomalies using binary trees) may be used in place of, or in combination with, a one-class SVM to determine which patches in previous positive patches 302D to remove as outliers.

Once SVM training engine <NUM> determines which previous positive patches 302D are in the same class as current positive patches 302B, in step 509E, both current positive patches 302B and the selected previous positive patches 302D (selected by the positive one-class SVM) are combined and stored as two-class positive training patches <NUM> (the patches may be stored as images or as identifiers to the relevant test patches <NUM>). These patches <NUM> are then, in turn, used to train two-class SVM <NUM>.

A similar process may then take place for negative patches. That is, in step 509F, current negative patches 302C may be accessed, similar to step 509A. In step <NUM>, previous negative patches 302E may be accessed, similar to step 509B. In step <NUM>, negative one-class SVM <NUM> may be trained, similar to step 509C. In step 509J, a subset of previous negative patches 302E may be determined, similar to step 509D. In step <NUM>, the current negative patches 302C may be combined with the subset of previous negative patches 302E and stored in two-class negative training patches <NUM>, similar to step 509E.

As an alternative, instead of using negative one-class SVM <NUM> to perform steps 509F-<NUM>, the positive one-class SVM <NUM> may be used for both positive and negative patches. That is, for example, if no current negative patches 302C have been provided by the user, the SVM training engine <NUM> may use positive one-class SVM <NUM> to determine the previous negative patches to use as two-class negative training patches <NUM>.

In particular, there would be no current negative patches to access in step 509F so that step would be skipped. In step <NUM>, previous negative patches 302E would be accessed. Step <NUM> could be skipped and, instead, the trained positive one-class SVM would be used. Step 509J would be modified, instead of selecting previous negative patches 302E that are within inner bands <NUM> and <NUM>, only previous negative patches 302E that are not within bands <NUM> and <NUM> would be selected. That is, only previous negative patches 302E that are not sufficiently similar to current positive patches 302B would be selected (bands <NUM>, etc.). The selected previous negative patches 302E (or identifiers thereof) would then be stored in two-class negative training patches <NUM>. If there are no previous negative patches 302E, SVM training engine <NUM> may store stock negative patches selected by users of other systems or never selected by any user but that have characteristics of negative patches. In the alternative, these stock negative patches may be stored in previous negative patches 302E.

Once the two-class positive training patches <NUM> and two-class negative training patches <NUM> have been determined, the SVM training engine <NUM> proceeds to train two-class SVM <NUM> with patches <NUM> and <NUM> in step <NUM>. That is, the two classes of two-class SVM <NUM> are positive patches and negative patches and will be stored by the respective positive and negative patches determined by one-class SVMs <NUM> and <NUM>.

In step <NUM>, once two-class positive training patches <NUM> (shown with one exception on the top right of <FIG>) and two-class negative training patches <NUM> (shown on the bottom left of <FIG>) have been provided to the (linear) two-class SVM <NUM>, the two-class SVM <NUM> selects negative support vectors (encircled patches the bottom dashed line crosses) and positive support vectors (encircled patches the top dashed line crosses) so as to maximize the margin width (the width between the two parallel dashed lines). The hyperplane is the solid line shown at the midpoint between the two dashed lines. <FIG> is similar.

Once the hyperplane is determined in two-class SVM <NUM> in step <NUM>, step <NUM> of <FIG> is complete.

In step <NUM>, the classification engine <NUM> provides as an input all or a subset of the test patches <NUM> to now trained two-class SVM <NUM>. The trained two-class SVM <NUM> then classifies each of test patches <NUM> as either being a positive patch (positive for cancer, for example) if the patch is on the positive side of the hyperplane, or a negative patch (negative for cancer, for example) if the patch is on the negative side of the hyperplane, as shown in <FIG>. Once step <NUM> is complete, each test patch <NUM> is marked as either positive or negative.

Once the classification of step <NUM> is complete, the system proceeds to step <NUM>, the grouping step. In step <NUM>, the test patches <NUM> that have been determined to be positive are grouped with other neighboring positive patches. Similarly, the test patches <NUM> that have been determined to be negative are grouped with other neighboring negative patches. Step <NUM> is described in further detail below, with reference to <FIG>.

In general, the process through generation of the convex hulls is as follows: (<NUM>) build patch grid map based on two class SVM (positive is <NUM>, negative is <NUM>); (<NUM>) convolve this map with adjacency kernel; (<NUM>) create primary blobs. Inside each blob, each patch must have a score of at least <NUM> and all patches being connected; (<NUM>) create supporting blobs, inside each supporting blob, each patch must have a score of at least <NUM> and all patches being connected; and (<NUM>) build convex hull for each of primary and supporting blobs.

At step 513A, the grouping engine <NUM> determines a probability score by analyzing each test patch <NUM> and its neighboring patches. Specifically, a score is determined as follows: start from <NUM> and add <NUM> if the patch itself has been classified as the relevant class (positive or negative), add <NUM> for each patch directly above, below, to the left, and to the right have been classified as the same class (i.e., positive or negative), and add <NUM> for each neighboring corner patch classified as the same class.

For example, <FIG> shows a representative portion of test patches <NUM>. The P in certain patches indicates that the two-class SVM <NUM> has determined that the patch is positive. Analyzing, for example, patch(<NUM>, <NUM>) (column <NUM>, row <NUM>), this patch would have a probability score of <NUM>. We add <NUM> because the patch itself (<NUM>, <NUM>) has been determined to be positive by the two-class SVM <NUM>. We add <NUM> for each of the patches on top, bottom, left and right of the relevant patch (<NUM>, <NUM>), (<NUM>, <NUM>), (<NUM>, <NUM>), and (<NUM>, <NUM>). And we add <NUM> for each of the <NUM> neighboring corner patches (<NUM>, <NUM>), (<NUM>, <NUM>), and (<NUM>, <NUM>). Since (<NUM>, <NUM>) is not indicated as positive, <NUM> is not added to the probability score for that patch. Thus, its probability score becomes <NUM>+<NUM>+<NUM>=<NUM>.

As another example, patch (<NUM>, <NUM>) would have a score of <NUM> (<NUM> for each of patches (<NUM>, <NUM>), (<NUM>, <NUM>), (<NUM>, <NUM>), and (<NUM>, <NUM>) and <NUM> each for corner patches (<NUM>, <NUM>) and (<NUM>, <NUM>)).

According to step 513B, once the above process has been performed for all test patches <NUM>, any patches with a probability score of <NUM> or above are considered primary blobs and any patches with a probability score of <NUM> and above are considered supporting areas (these numbers are just example and different numbers may be used). More specifically, two binary maps are generated: primary and supporting. The primary binary map includes only patches with a score of <NUM> and above and the supporting binary map includes only patches with a score of <NUM> and above.

In step 513C, the grouping engine <NUM> generates a convex hull, line string, and point for all primary blobs and assigns a probability score of <NUM> (or the highest score of any patch within the convex hull) for all patches within the convex hull.

<FIG> illustrates a generated convex hull around multiple positive patches (or negative patches, as the case may be. The process of <FIG> is performed for positive patches and then for negative patches).

In step 513D, the grouping engine <NUM> generates a convex hull, line string, and point for supporting blobs and assigns a probability score of <NUM> (or the highest score of any patch within the convex hull) for all patches within the convex hull.

In step 513E, the grouping engine <NUM> merges supporting blobs with primary blobs if the primary blobs and supporting blobs have overlapping geometry. The overlapping geometry determination is made if one of the vertices of one convex hull (for example, a primary blob convex hull) is inside the other convex hull (for example, a supporting blob/area convex hull). That it is, for each vertex in one hull, the system determined if it is inside any other convex hull. If no vertex is inside another convex hull, the two convex hulls do not overlap.

In step 513F, if the convex hull, line string or point intersect, the grouping engine <NUM> groups the convex hulls together using largest overlapping criteria.

In step <NUM>, the probability of each of the primary blobs is determined as follows: Probability primary = k * primary_patch_count, where k is a constant such as <NUM>, <NUM>, <NUM>, <NUM>. <NUM>, or <NUM> and primary_patch_count is the number of patches in a particular primary blob. The constant k may be set by the user or be predetermined. For example, if k is <NUM>,<NUM> and the primary_patch_count is <NUM>, the probability is then <NUM>*<NUM> = <NUM>. The purpose of the above calculation is to take into account that blobs that have been identified as likely containing positive patches that include a larger number of patches are more likely to be positive than blobs with a lower number of patches.

In step <NUM>, the probability score of each of the supporting areas/blobs being merged into a primary blob is determined as follows: Probability supporting = k * supporting_patch_count/j, where k is a constant such as <NUM>, <NUM>, <NUM>, <NUM>. <NUM>, or <NUM>, supporting_patch_count is the patch count of the number of patches in all supporting blobs being merged into a particular primary blob, and j is a constant such as <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM>. For example, if k is <NUM>,<NUM>, the supporting_patch_count is <NUM>, and j is <NUM>, the probability is then <NUM> * <NUM>/<NUM>=<NUM>. Finally, if Probability supporting>= k or another constant, the Probability supporting is set to k. For example, in the above example, since Probability supporting was determined to be <NUM>, Probability supporting would then be set to the value of k, <NUM>,.

In step 513J, the final probability of the primary blob and the supporting areas/blobs merged into the primary blob is determined as follows: Probability final = Probability primary + Probability supporting. For example, if Probability primary is <NUM> and Probability supporting is <NUM>, the final probability is <NUM>. However, if the final probability is larger than <NUM>, the final probability is then set to <NUM>. Thus, in the above example, the final probability would be set to <NUM>.

In step <NUM>, the grouping engine <NUM> uses the final grouped convex hulls to reassign the probability to the largest score of any of the convex hulls within the group. The scores of the convex hulls are then stored in regions of interest <NUM> and, for any convex hull above a threshold such as <NUM>, such convex hulls are shown in green in <FIG> and <FIG>to indicate a high likelihood of either positive or negative patches. For example, green may indicate a high likelihood of positive patches. Examples of the present specification may be used to indicate a high likelihood of positive patches of various characteristics of the sample/image. For example, in <FIG> and <FIG>, the green indicates a high likelihood of positive patches of cancer, whereas the green in <FIG> indicates a high likelihood of lymphocytes.

A region of interest (ROI) <NUM> (which may include multiple regions) is then generated based on the output from step <NUM>. The regions of interest <NUM> may include a tissue mask such as a microdissection mask, which may be used in conducting a laser microdissection in order to excise a target ROI for further processing. Only certain ROIs may be used as a microdissection mask based on the size of the ROI and the quality of the ROI. That is, certain ROIs may not be suitable for microdissection.

Examples described herein implement few-shot learning in combination with a pathologist's input to more efficiently and quickly generate classification of tissue regions of interest in a sample. A pathologist no longer must manually outline all regions of interest in an image. Rather, a system using the few shot learning techniques described herein helps accelerate the pathologist's work by at least partially automating the process of identifying regions of interest. In another example, output of the disclosed examples is used as training data for training a deep learning network (such as CNN <NUM> shown in <FIG>) or other machine-learning system that requires large amounts of training data. This allows training data to be generated more efficiently.

Claim 1:
A computer implemented method of generating at least one shape of a region of interest (<NUM>) in a digital image, the method comprising:
obtaining (<NUM>), by an image processing engine (<NUM>), access to a digital tissue image of a biological sample;
tiling (<NUM>), by the image processing engine, the digital tissue image into a collection of image patches (<NUM>);
obtaining (<NUM>), by the image processing engine, a plurality of features from each patch in the collection of image patches, the plurality of features defining a patch feature vector in a multidimensional feature space including the plurality of features as dimensions;
determining (<NUM>), by the image processing engine, a user selection of a user selected subset of patches (302B, 302C) in the collection of image patches;
characterized by
training (509C, <NUM>) a one-class support vector machine (<NUM>, <NUM>) based on the user selected subset of patches;
accessing (509B, <NUM>) all or a predetermined number of previously selected patches (302D, 302E), and determining (509D, 509J), using the trained one-class support vector machine, a subset of the previously selected patches which are in a same class as the user selected subset of patches;
training (<NUM>) a two-class support vector machine (<NUM>) based on the user selected subset of patches and the subset of the previously selected patches (<NUM>, <NUM>);
classifying (<NUM>), by applying the trained two-class support vector machine to patch vectors of other patches in the collection of patches, the other patches as belonging or not belonging to the same class of interest as the user selected subset of patches; and
identifying (<NUM>) one or more regions of interest based at least in part on the results of the classifying.