Abstract: Methods for segmenting medical images from different modalities include integrating a plurality of types of quantitative image descriptors with a deep 3D convolutional neural network. The descriptors include: (i) a Gibbs energy for a prelearned 7th-order Markov-Gibbs random field (MGRF) model of visual appearance, (ii) an adaptive shape prior model, and (iii) a first-order appearance model of the original volume to be segmented. The neural network fuses the computed descriptors to obtain the final voxel-wise probabilities of the goal regions.

Abstract: An automated segmentation system for medical imaging data segments data into muscle and fat volumes, and separates muscle volumes into discrete muscle group volumes using a plurality of models of the medical imaging data, and wherein the medical imaging data includes data from a plurality of imaging modalities.

Abstract: Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic system to assess pulmonary function and risk of mortality in patents with coronavirus disease 2019. The CAD system processes thoracic X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients. An alternative approach is to use an automatic CAD system to grade COVID-19 from computed tomography (CT) images to determine an accurate diagnosis of lung function.

Abstract: An automated segmentation system for medical imaging data segments data into muscle and fat volumes, and separates muscle volumes into discrete muscle group volumes using a plurality of models of the medical imaging data, and wherein the medical imaging data includes data from a plurality of imaging modalities.

Abstract: A novel system and method for accurate detection and quantification of fibrous tissue produces a virtual medical image of tissue treated with a second stain based on a received medical image of tissue treated with a first stain using a computer-implemented trained deep learning model. The model is trained to learn the deep texture patterns associated with collagen fibers using conditional generative adversarial networks to detect and quantify fibrous tissue.

Abstract: Systems and methods for diagnosing prostate cancer. Image sets (e.g., MRI collected at one or more b-values) and biological values (e.g., prostate specific antigen (PSA)) have features extracted and integrated to produce a diagnosis of prostate cancer. The image sets are analyzed primarily in three steps: (1) segmentation, (2) feature extraction, smoothing, and normalization, and (3) classification. The biological values are analyzed primarily in two steps: (1) feature extraction and (2) classification. Each analysis results in diagnostic probabilities, which are then combined to pass through an additional classification stage. The end result is a more accurate diagnosis of prostate cancer.

Abstract: A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.

Abstract: Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic system to assess pulmonary function and risk of mortality in patents with coronavirus disease 2019. The CAD system processes thoracic X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients.

Abstract: Computer-implemented systems and methods for automated diagnosis of diabetic retinopathy apply machine learning techniques to clinical and demographic data combined with optical coherence tomography and optical coherence tomography angiography image data to diagnose and grade diabetic retinopathy.

Abstract: A computer aided diagnostic system and automated method to classify a kidney utilizes medical image data and clinical biomarkers in evaluation of kidney function pre- and post-transplantation. The system receives image data from a medical scan that includes image data of a kidney, then segments kidney image data from other image data of the medical scan. The kidney is then classified by analyzing at least one feature determined from the kidney image data and the at least one clinical biomarker.

Abstract: A computer-aided diagnostic (CAD) system and method for non-invasive detection of cancer includes receiving and analyzing data from a plurality of sources, using a neural network to generate an initial classification probability from each data source, assigning weights to the initial classification probabilities, and integrating the initial classification probabilities to generate a final classification. The final classification may be a designation of a tissue, such as a pulmonary nodule, as cancerous or noncancerous.

Abstract: A non-invasive computer-aided diagnosis system generates a diagnosis of mild cognitive impairment, a disease state which often leads to the development of Alzheimer's disease. The system uses as inputs both functional positron emission tomography and structural magnetic resonance imaging data, reconstructs a model of the patient's cortex, uses machine-learning techniques to generate probabilities for mild cognitive impairments for local cortical regions, uses machine-learning techniques to fuse the local diagnoses to generate a global diagnosis based on each imaging modality, then uses machine-learning techniques to fuse the modality-specific global diagnoses to generate a final global diagnosis.

Abstract: An automated segmentation system for medical imaging data segments data into muscle and fat volumes, and separates muscle volumes into discrete muscle group volumes using a plurality of models of the medical imaging data, and wherein the medical imaging data includes data from a plurality of imaging modalities.

Abstract: A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.

Abstract: Systems and methods for diagnosing prostate cancer. Image sets (e.g., MRI collected at one or more b-values) and biological values (e.g., prostate specific antigen (PSA)) have features extracted and integrated to produce a diagnosis of prostate cancer. The image sets are analyzed primarily in three steps: (1) segmentation, (2) feature extraction, smoothing, and normalization, and (3) classification. The biological values are analyzed primarily in two steps: (1) feature extraction and (2) classification. Each analysis results in diagnostic probabilities, which are then combined to pass through an additional classification stage. The end result is a more accurate diagnosis of prostate cancer.

Abstract: There is provided a method of processing a cerebrovascular medical image, the method comprising receiving magnetic resonance angiography (MRA) image associated with a cerebrovascular tissue comprising blood vessels and brain tissues other than blood vessels; segmenting MRA image using a prior appearance model for generating first prior appearance features representing a first-order prior appearance model and second appearance features representing a second-order prior appearance model of the cerebrovascular tissue, wherein current appearance model comprises a 3D Markov-Gibbs Random Field (MGRF) having a 2D rotational and translational symmetry such that MGRF model is 2D rotation and translation invariant; segmenting MRA image using current appearance model for generating current appearance features distinguishing blood vessels from other brain tissues; adjusting MRA image using first and second prior appearance features and current appearance futures; and generating an enhanced MRA image based on said adjustm

Abstract: Methods for segmenting medical images from different modalities include integrating a plurality of types of quantitative image descriptors with a deep 3D convolutional neural network. The descriptors include: (i) a Gibbs energy for a prelearned 7th-order Markov-Gibbs random field (MGRF) model of visual appearance, (ii) an adaptive shape prior model, and (iii) a first-order appearance model of the original volume to be segmented. The neural network fuses the computed descriptors to obtain the final voxel-wise probabilities of the goal regions.

Abstract: A system and computation method is disclosed that identifies radiation-induced lung injury after radiation therapy using 4D computed tomography (CT) scans. After deformable image registration, the method segments lung fields, extracts functional and textural features, and classifies lung tissues. The deformable registration locally aligns consecutive phases of the respiratory cycle using gradient descent minimization of the conventional dissimilarity metric. Then an adaptive shape prior, a first-order intensity model, and a second-order lung tissues homogeneity descriptor are integrated to segment the lung fields. In addition to common lung functionality features, such as ventilation and elasticity, specific regional textural features are estimated by modeling the segmented images as samples of a novel 7th-order contrast-offset-invariant Markov-Gibbs random field (MGRF). Finally, a tissue classifier is applied to distinguish between the injured and normal lung tissues.

Abstract: A non-invasive computer-aided diagnosis system generates a diagnosis of mild cognitive impairment, a disease state which often leads to the development of Alzheimer's disease. The system uses as inputs both functional positron emission tomography and structural magnetic resonance imaging data, reconstructs a model of the patient's cortex, uses machine-learning techniques to generate probabilities for mild cognitive impairments for local cortical regions, uses machine-learning techniques to fuse the local diagnoses to generate a global diagnosis based on each imaging modality, then uses machine-learning techniques to fuse the modality-specific global diagnoses to generate a final global diagnosis.

Abstract: There is provided a method of processing a cerebrovascular medical image, the method comprising receiving magnetic resonance angiography (MRA) image associated with a cerebrovascular tissue comprising blood vessels and brain tissues other than blood vessels; segmenting MRA image using a prior appearance model for generating first prior appearance features representing a first-order prior appearance model and second appearance features representing a second-order prior appearance model of the cerebrovascular tissue, wherein current appearance model comprises a 3D Markov-Gibbs Random Field (MGRF) having a 2D rotational and translational symmetry such that MGRF model is 2D rotation and translation invariant; segmenting MRA image using current appearance model for generating current appearance features distinguishing blood vessels from other brain tissues; adjusting MRA image using first and second prior appearance features and current appearance futures; and generating an enhanced MRA image based on said adjustm