Abstract: An information processing apparatus includes a first acquisition unit that acquires a user's instruction to change medical information to be displayed on a display unit, a second acquisition unit that acquires a plurality of pieces of medical information to be sequentially displayed on the display unit, a group generation unit that groups the acquired plurality of pieces of medical information based on the acquired user's instruction, a third acquisition unit configured to acquire representative information indicating the medical information contained in the group generated by the group generation unit, and an output unit configured to output the acquired representative information to the display unit.

Abstract: A downloadable navigator for a mobile ultrasound unit having an ultrasound probe, implemented on a portable computing device. The navigator includes a trained orientation neural network to receive a non-canonical image of a body part from the mobile ultrasound unit and to generate a transformation associated with the non-canonical image, the transformation transforming from a position and rotation associated with a canonical image to a position and rotation associated with the non-canonical image; and a result converter to convert the transformation into orientation instructions for a user of the probe and to provide and display the orientation instructions to the user to change the position and rotation of the probe.

Abstract: A visualization system including multiple light sources, an image sensor configured to detect imaging data from the multiple light sources, and a control circuit is disclosed. At least one of the light sources is configured to emit a pattern of structured light. The control circuit is configured to receive the imaging data from the image sensor, generate a three-dimensional digital representation of the anatomical structure from the pattern of structured light detected by the imaging data, obtain metadata from the imaging data, overlay the metadata on the three-dimensional digital representation, receive updated imaging data from the image sensor, and generate an updated three-dimensional digital representation of the anatomical structure based on the updated imaging data. The visualization system can be communicatively coupled to a situational awareness module configured to determine a surgical scenario based on input signals from multiple surgical devices.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for contrast enhanced ultrasound quantification imaging are provided.

Abstract: Provided are an image fusion classification method and device. The method includes that: a three-dimensional weight matrix of a hyperspectral image is obtained by use of a Support Vector Machine (SVM) classifier (101); superpixel segmentation is performed on the hyperspectral image to obtain K superpixel images, K being a positive integer (102); the three-dimensional weight matrix is regularized by use of a superpixel-image-based segmentation method to obtain a regular matrix (103); and a class that a sample belongs to is determined according to the regular matrix (104).

Abstract: A combination unit generates a plurality of composite two-dimensional images from a plurality of tomographic images acquired by performing tomosynthesis imaging on an object using different generation methods. In this case, the combination unit generates a first composite two-dimensional image having a quality corresponding to a two-dimensional image acquired by simple imaging or a second composite two-dimensional image in which a structure included in the object has been highlighted as at least one of the plurality of composite two-dimensional images.

Abstract: Various methods and systems are provided for artifact reduction with resolution preservation. In one example, a method includes obtaining projection data of an imaging subject, identifying a metal-containing region in the projection data, interpolating the metal-containing region to generate interpolated projection data, extracting high frequency content information from the projection data in the metal-containing region, adding the extracted high frequency content information to the interpolated projection data to generate adjusted projection data, and reconstructing one or more diagnostic images from the adjusted projection data.

Abstract: A high efficiency method of processing images to provide perceptual high-contrast output. Pixel intensities are calculated by a weighted combination of a fixed number of static bounding rectangle sizes. This is more performant than incrementally growing the bounding rectangle size and performing expensive analysis on resultant histograms. To mitigate image artifacts and noise, blurring and down-sampling are applied to the image prior to processing.

Abstract: A method, a system and a computer readable medium for automatic segmentation of a 3D medical image, the 3D medical image comprising an object to be segmented, the method characterized by comprising: carrying out, by using a machine learning model, in at least two of a first, a second and a third orthogonal orientation, 2D segmentations for the object in slices of the 3D medical image to derive 2D segmentation data; determining a location of a bounding box (10) within the 3D medical image based on the 2D segmentation data, the bounding box (10) having predetermined dimensions; and carrying out a 3D segmentation for the object in the part of the 3D medical image corresponding to the bounding box (10).

Abstract: A system and method include training of an artificial neural network to generate an output data set, the training based on the plurality of sets of emission data acquired using a first imaging modality and respective ones of data sets acquired using a second imaging modality.

Abstract: In accordance with at least some embodiments of the present disclosure, a process to improve computed tomography (CT) to cone beam computed tomography (CBCT) registration is disclosed. The process may include receiving a CT image generated by CT-scanning of an object, and receiving a CBCT image generated by CBCT-scanning of the object. The process may include generating an image mask based on Digital Imaging and Communications in Medicine (DICOM) information extracted from the CBCT image. For a specific pixel in the CBCT image, the image mask contains a corresponding data-field indicating whether the specific pixel contains image data generated based on the CBCT-scanning of the object. The process may further include generating a registered image by utilizing the image mask to perform a DIR between the CT image and the CBCT image.

Abstract: A method for the artifact correction of three-dimensional volume image data of an object is disclosed. In an embodiment, the method includes receiving first volume image data via a first interface, the first volume image data being based on projection measurement data acquired via a computed tomography device, the computed tomography device including a system axis, and the first volume image data including an artifact including high-frequency first portions in a direction of a system axis and including second portions, being low-frequency relative to the high-frequency first portions, in a plane perpendicular to the system axis; ascertaining, via a computing unit, artifact-corrected second volume image data by applying a trained function to the first volume image data received; and outputting the artifact-corrected second volume image data via a second interface.

Abstract: A method for magnetic resonance imaging (MRI) is provided. The method may include obtaining scan data of a subject. The scan data may be acquired by an MR scanner at a time according to a pulse sequence. The method may include obtaining motion data of the subject. The motion data of the subject may be acquired by one or more sensors at the time. The motion data may reflect a motion state of the subject at the time. The method may also include determining, based on the motion data of the subject, a processing strategy indicating whether using the scan data to fill one or more k-space lines corresponding to the pulse sequence in a k-space. The method may further include obtaining k-space data based on the processing strategy.

Abstract: A device and a method for detecting a tumor using a medical image and diagnosing a shape and a property of the detected tumor are disclosed. An exemplary medical image-based tumor detection and diagnostic device includes: an input unit configured to obtain a medical image related to a patient; a preprocessing unit configured to preprocess the obtained medical image to observe a tumor region; an analysis unit configured to divide the preprocessed image into a plurality of regions by applying a deep neural network-based deep learning technique; and a measurement unit configured to group the plurality of divided regions by performing clustering on the plurality of divided regions. The measurement unit extracts a group feature value in respect to each of the grouped regions and derives diagnosis information related to the tumor based on the extracted group feature value.

Abstract: Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

Abstract: A method for improving an image quality of X-ray tomograms includes generating a low-pass filtered X-ray tomogram by applying a low-pass filter to a two-dimensional X-ray tomogram. The low-pass filter is only applied to pixels with image values lying within a predetermined image value interval. A high-pass filtered X-ray tomogram is generated by subtracting the low-pass filtered X-ray tomogram from the two-dimensional X-ray tomogram. A Radon transform image is generated by calculating a Radon transform of the high-pass filtered X-ray tomogram. A modified Radon transform image is generated by modifying values of the pixels of the Radon transform image with values lying outside a predetermined value interval. A modified high-pass filtered X-ray tomogram is generated by calculating an inverse Radon transform of the modified Radon transform image. A modified X-ray tomogram is generated by the addition of the modified high-pass filtered X-ray tomogram to the low-pass filtered X-ray tomogram.

Abstract: The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

Abstract: Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

Abstract: A medical navigation system including a controller configured to: generate a three-dimensional (3D) volume based upon acquired image information of a region of interest (ROI), determine a reference path (RP) to an object-of-interest (OOI) situated within the ROI, the RP defining an on-road path (ONP) through at least one natural pathway of an organ subject to cyclical motion and an adjacent off-road path (ORP) through tissue of the organ leading to the OOI, and an exit point situated between the ONP and the ORP, query an SSD within the at least one natural pathway to obtain SSDI, determine a shape and a pose of one or more portions of the SSD in accordance with the SSDI, calculate an error between the RP and the determined shape and pose of the SSD, and/or determine when or where to exit a wall of the natural pathway and begin the ORP based upon the calculated error.

Abstract: Provided are a method and apparatus for correcting scattering artifacts in industrial three-dimensional (3D) cone beam computed tomography (CT) that may prepare raw data acquired from a subject through computed tomography (CT) and a primary signal acquired from shape prior information of the subject, may estimate a scatter kernel based on the raw data and the primary signal, may acquire result data by removing, from the raw data, a scatter signal estimated based on the scatter kernel, and may generate an image from the result data.

Abstract: A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

Abstract: Embodiments discussed herein facilitate generation of a prognosis for recurrence or non-recurrence of atrial fibrillation (AF) after pulmonary vein isolation (PVI). A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prognosis for AF after PVI based on radiographic images, alone or in combination with clinical features. A second set of embodiments discussed herein relates to determination of a prognosis for a patient for AF after PVI based on radiographic images, alone or in combination with clinical features.

Abstract: Systems and techniques for generating and/or employing a medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate output data regarding a brain anatomical region based on diffusion-weighted imaging (DWI) data associated with the brain anatomical region and apparent diffusion coefficient (ADC) data associated with the brain anatomical region. The system also detects presence or absence of a medical stroke condition associated with the brain anatomical region based on the output data.

Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

Abstract: A method for performing positron emission tomography (PET) image denoising using a dilated convolutional neural network system includes: obtaining, as an input to the dilated convolutional neural network system, a noisy image; performing image normalization to generate normalized image data corresponding to the noisy image; encoding the normalized image data using one or more convolutions in the dilated convolutional neural network, whereby a dilation rate is increased for each encoding convolution performed to generate encoded image data; decoding the encoded image data using one or more convolutions in the dilated convolutional neural network, whereby dilation rate is decreased for each decoding convolution performed to generate decoded image data; synthesizing the decoded image data to construct a denoised output image corresponding to the noisy image; and displaying the denoised output image on an image display device, the denoised output image having enhanced image quality compared to the noisy image.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions that are executed by the at least one processor and configure the at least one processor to implement a medical record to illustrative medical image translation engine. The medical record to illustrative medical image translation engine receives a medical record batch from storage for a patient and generates one or more predicted prognosis records based on the medical record batch using a neural network. The medical record to illustrative medical image translation engine converts the one or more predicted prognosis records to illustrative medical images using a first agent. The medical record to illustrative medical image translation engine generates a presentation of disease progression using the illustrative medical images and outputs the presentation to a user.

Abstract: Systems and methods for iteratively computing an image registration or an image segmentation are driven by an optimization function that includes a similarity measure component whose effect on the iterative computations is relatively mitigated based on a monitoring of volume changes of volume elements at image locations during the iterations. A system and a related method quantify a registration error by applying a series of edge detectors to input images and combining related filter responses into a combined response. The series of filters are parameterized with a filter parameter. An extremal value of the combined response is then found and a filter parameter associated with said extremal value is then returned as output. This filter parameter relates to a registration error at a given image location.

Abstract: Discussed herein are devices, systems, and methods for multi-image ground control point (GCP) determination. A method can include extracting, from a first image including image data of a first geographical region, a first image template, the first image template including a contiguous subset of pixels from the first image and a first pixel of the first image indicated by the GCP, predicting a first pixel location of the GCP in a second image, the second image including image data of a second geographical overlapping with the first geographical region, extracting, from the second image, a second image template, the second image template including a contiguous subset of pixels from the second image and a second pixel corresponding to the pixel location, identifying a second pixel of the second image corresponding to a highest correlation score, and adding a second pixel location of the identified pixel to the GCP.

Abstract: A nuclear medicine (NM) multi-head imaging system is provided that includes a gantry, plural detector units mounted to the gantry, and at least one processor. The at least one processor is operably coupled to at least one of the detector units, and configured to acquire, via the detector units, imaging information. The imaging information includes edge information and interior information. The edge information corresponds to a contour boundary of tissue and the interior information corresponds to an intermediate portion of the tissue. The least one processor is configured to control the detector units to acquire a proportionally larger amount of imaging information for the contour boundary than for the intermediate portion.

Abstract: For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. The machine-learnt multi-task generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. The machine-learnt multi-task generator is trained to output both the 3D segmentation and a complete volume. The 3D segmentation may be used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

Abstract: Apparatus for diagnosing breast cancer, the apparatus comprising a controller having a set of instructions executable to: acquire a contrast enhanced region of interest (CE-ROI) in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; determine a texture neighborhood for each of a plurality of X-ray pixels in the CE-ROI, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel; generate a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI that are located within the texture neighborhood; and use a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant.

Abstract: Techniques are disclosed for measuring the corpus callosum volume of a fetus using magnetic resonance imaging. A scanogram of a fetus is acquired, and a detection area is determined using the corpus callosum position of the fetus in the scanogram. Magnetic resonance scanning is performed on the detection area to obtain a diffusion weighted image, with a gradient direction that is orthogonal or normal to an extending direction of fiber bundles of the corpus callosum. A fetal head image is cropped in the diffusion weighted image, and a predetermined threshold is applied to obtain an image including pixels having a brightness value that is greater than the threshold. Image processing is performed on the binarized image, with the largest region therein being identified as the corpus callosum, and the sum of voxel dimensions associated with the signal of the largest region being calculated as the corpus callosum volume.

Abstract: A medical information processing apparatus includes processing circuitry. The processing circuitry is configured to acquire medical image data representing a blood vessel of a subject, extract a blood vessel shape from the medical image data, determine degree of meandering in each of regions from the extracted blood vessel shape, specify a deformed region in which the degree of meandering changes due to insertion of a device into the blood vessel on the basis of the degree of meandering, and output the deformed region in the blood vessel.

Abstract: A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator component (126) configured to determine a fractional flow reserve value via simulation and a traffic light engine (128) configured to track a user-interaction with the computing system at one or more points of the simulation to determine the fractional flow reserve value. A processor (120) is configured to execute the biophysical simulator component to determine the fractional flow reserve value and configured to execute the traffic light engine to track the user-interaction with respect to determining the fractional flow reserve value and provide a warning in response to determining there is a potential incorrect interaction. A display is configured to display the warning requesting verification to proceed with the simulation from the point, wherein the simulation is resumed only in response to the processor receiving the requested verification.

Abstract: Ultrasound imaging system, devices, and methods for minimizing grating lobe artefacts in an ultrasound image are provided. For example, an ultrasound imaging system can include an array of acoustic elements and a processor in communication with the array. The processor controls the array to activate a plurality of apertures and subapertures in a scan sequence, generate an image comprising a plurality of pixels, identify at least one subaperture of the plurality of subapertures corresponding to a reduced signal value for one or more pixels of the image, and generate a grating-lobe-minimized image based on the identified subapertures. The grating-lobe-minimized image can be output to a display or combined with the original ultrasound image to include image features lost or reduced in the grating-lobe-minimized image. The grating-lobe-minimized image advantageously reduces image artefacts and clutter to simplify ultrasound image analysis and diagnosis procedures.

Abstract: A medical imaging system includes a movable station having a C-arm, a collector, an X-ray beam emitter, and a controller. The collector is attached to a first end of the C-arm. The X-ray beam emitter faces the collector to emit an X-ray beam in a direction of the collector and is attached to a second end of the C-arm. The controller moves one of the X-ray beam emitter and the collector to a first offset position along a lateral axis orthogonal to the arc, and obtains a first set of images by rotating the collector and the X-ray beam emitter along the arc about a scanned volume. The controller moves the one of the X-ray beam emitter and the collector to a second offset position along the lateral axis, and obtains a second set of images. The controller combines the first and second set of images to generate a three-dimensional image of the scanned volume.

Abstract: Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

Abstract: A magnetic resonance (MR) imaging method of detecting and scoring motion artifacts in MR images of an object is provided. The method includes computing a k-space difference map based at least in part on first MR signals of the object acquired with a first coil and second MR signals of the object acquired with a second coil. The method also includes generating a difference plot based on the k-space difference map, the difference plot including a curve. The method further includes calculating a motion score based on the curve in the difference plot, wherein the motion score indicates the level of motion artifacts in the image caused by motion of the object during acquisition of the first MR signals and the second MR signals, and the motion score includes an area under the curve. Moreover, the method includes outputting the motion score.

Abstract: A method for correcting projection images in CT image reconstruction is provided. The method may include obtaining a plurality of projection images of a subject. Each of the plurality of projection images may correspond to one of the plurality of gantry angles. The method may further include correcting a first projection image of the plurality of projection images according to a process for generating a corrected projection image. The process may include performing, based on the first projection image and a second projection image of the plurality of projection images, a first correction on the first projection image to generate a preliminary corrected first projection image. The process may also include performing, based on at least part of the preliminary corrected first projection image, a second correction on the preliminary corrected first projection image to generate a corrected first projection image corresponding to the first gantry angle.

Abstract: A bullseyes plot may be generated based on cardiac magnetic resonance imaging (CMRI) to facilitate the diagnosis and treatment of heart diseases. Described herein are systems, methods, and instrumentalities associated with bullseyes plot generation. A plurality of myocardial segments may be obtained for constructing the bullseye plot based on landmark points detected in short-axis and long-axis magnetic resonance (MR) slices of the heart and by arranging the short-axis MR slices sequentially in accordance with the order in which the slices are generated during the CMRI. The sequential order of the short-axis MR slices may be determined utilizing projected locations of the short-axis MR slices on a long-axis MR slice and respective distances of the projected locations to a landmark point of the long-axis MR slice. The myocardium and/or landmark points may be identified in the short-axis and/or long-axis MR slices using artificial neural networks.

Abstract: Systems and methods can include a method for training a deep convolutional neural network to provide a patient radiation treatment plan, the method comprising collecting patient data from a group of patients, the patient data including at least one image of patient anatomy and a prior treatment plan, wherein the treatment plan includes predetermined machine parameters, and training a deep convolution neural network for regression by using the prior treatment plans and the corresponding collected patient data to determine a new treatment plan.

Abstract: A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. A closed-loop color conversion process is used to improve compression while taking into consideration distortion introduced throughout the point cloud compression process.

Abstract: An image analysis device 10 includes: a generation unit 11 which generates a similar set, which is a set of similar pieces of learning data selected from among a plurality of pieces of learning data, each including an image and information that represents an object to be recognized that is displayed in the image; and a learning unit 12 which uses the generated similar set to learn parameters for a predetermined recognition model that allow the predetermined recognition model to recognize the object to be recognized that is displayed in each image included in the generated similar set.

Abstract: The present invention relates to a method for the evaluation of tissue gadolinium deposition that offers advantages compared with known methods. Comparison of different gadolinium-based contrast agents (GBCAs) based on retention, organ distribution, washout and safety is facilitated using the methods of the present invention.

Abstract: The present disclosure relates to an image information generation method, a pulse wave measurement system and an electronic device. The method comprises: with respect to a target part, acquiring a first infrared image sequence, each infrared image in the first infrared image sequence including at least a vein pattern; by registering the vein pattern in each infrared image in the first infrared image sequence, correcting each infrared image in the first infrared image sequence, thereby obtaining the corrected first infrared image sequence; removing at least the vein regions from respective infrared images in the corrected first infrared image sequence, to obtain image information of remaining regions as image information for pulse wave measurement.

Abstract: A method for acquiring image data obtains, for an intraoral feature, optical coherence tomography (OCT) data in three dimensions wherein at least one dimension is pseudo-randomly or randomly sampled and reconstructs an image volume of the intraoral feature using compressive sensing, wherein data density of the reconstructed image volume is larger than that of the obtained OCT data in the at least one dimension or according to a corresponding transform. The method renders the reconstructed image volume for display.

Abstract: A biological tissue analyzing device configured to analyze a biological tissue using hyperspectral data in which spectral information is associated with each of pixels forming a two-dimensional image and comprising the following (i) and (ii), as well as comprising (iii) and/or (iv): (i) a hyperspectral data acquisition unit configured to acquire the hyperspectral data; (ii) an analysis target region extraction unit configured to extract pixels corresponding to an analysis target region from a two-dimensional image of the biological tissue; (iii) an altered state classification unit configured to roughly classify an altered state of the biological tissue with unsupervised learning; and (iv) an altered state identification unit configured to identify the altered state of the biological tissue with supervised learning.

Abstract: A method for automatically classifying emission tomographic images includes receiving original images and a plurality of class labels designating each original image as belonging to one of a plurality of possible classifications and utilizing a data generator to create generated images based on the original images. The data generator shuffles the original images. The number of generated images is greater than the number of original images. One or more geometric transformations are performed on the generated images. A binomial sub-sampling operation is applied to the transformed images to yield a plurality of sub-sampled images for each original image. A multi-layer convolutional neural network (CNN) is trained using the sub-sampled images and the class labels to classify input images as corresponding to one of the possible classifications. A plurality of weights corresponding to the trained CNN are identified and those weights are used to create a deployable version of the CNN.

Abstract: A method of creating medical documentation uses selection of images representing synonyms for complex medical concepts. The images can be of two types, one corresponding to a parameter or Key in a Key/Value pair and the other corresponding to possible values of the selected parameter or Key. The user first selects an image or portion thereof corresponding to the parameter they wish to record observations, such as the heart or a valve of the heart. A set of images representing possible values of the parameter is then displayed e.g., images representing possible murmurs the selected valve may have. The user selects the image representing the value corresponding to the observation of the patient. A document is created by recording either the Key/Value image pairs or, alternative, text representing the synonyms for the images. Other methods of recording the document are possible, such as instantiating a set of class objects.