Abstract: A method for automatically estimating cellularity in a digital pathology slide image includes: extracting patches of interest from the digital pathology slide image; operating on each patch using a trained first deep convolutional neural network (DCNN) to classify that patch as either normal, having an estimated cellularity of 0%, or suspect, having a cellularity roughly estimated to be greater than 0%; operating on each suspect patch using a second DCNN, trained using a deep ordinal regression model, to determine an estimated cellularity score for that suspect patch; and combining the estimated cellularity scores of the patches of interest to provide an estimated cellularity for the digital pathology slide image at a patch-by-patch level.

Abstract: A method of tumor detection and segmentation accepts a first Whole Slide Image (WSI) having a first resolution; creates a corresponding second WSI having a second resolution lower than the first resolution; applies an adaptive thresholding technique to the second WSI to create a background removal mask background; applies the mask to the first WSI to provide a third WSI with extracted patches, characterized by a third resolution, greater than the second resolution and lower than the first resolution; uses a first machine learning system on the third WSI to create a heat map at the third resolution, indicating a subset of the patches likely to include one or more clusters of tumor cells; and uses a second machine learning system on the first WSI and the heat map to segment each patch in a corresponding output image at the first resolution, outlining one or more corresponding clusters.

Abstract: A method for automatically estimating cellularity in a digital pathology slide image includes: extracting patches of interest from the digital pathology slide image; operating on each patch using a trained first deep convolutional neural network (DCNN) to classify that patch as either normal, having an estimated cellularity of 0%, or suspect, having a cellularity roughly estimated to be greater than 0%; operating on each suspect patch using a second DCNN, trained using a deep ordinal regression model, to determine an estimated cellularity score for that suspect patch; and combining the estimated cellularity scores of the patches of interest to provide an estimated cellularity for the digital pathology slide image at a patch-by-patch level.

Abstract: System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.

Abstract: A method of tumor detection and segmentation accepts a first Whole Slide Image (WSI) having a first resolution; creates a corresponding second WSI having a second resolution lower than the first resolution; applies an adaptive thresholding technique to the second WSI to create a background removal mask background; applies the mask to the first WSI to provide a third WSI with extracted patches, characterized by a third resolution, greater than the second resolution and lower than the first resolution; uses a first machine learning system on the third WSI to create a heat map at the third resolution, indicating a subset of the patches likely to include one or more clusters of tumor cells; and uses a second machine learning system on the first WSI and the heat map to segment each patch in a corresponding output image at the first resolution, outlining one or more corresponding clusters.

Abstract: A method for automatic organ segmentation in CT images comprises in a first step, rough region segmentation; in a second step, coarse organ segmentation; and in a third step, refinement of organ segmentation. The organ may be a liver. Rough region segmentation may comprise applying standard anatomical knowledge to the CT images. Coarse segmentation may comprise identifying organ voxels using a probabilistic model. Refinement of organ segmentation may comprise refinement based on intensity, followed by refinement based on shape. Apparatuses configured to carry out the method are also disclosed.

Abstract: An assistive apparatus for organ localization, includes storing a 3D representation and CT images of an anatomical portion of the body of a subject. A localization circuitry determines a rib region and a spine region in the CT images and calculates first and second number of voxels within a first and second region of the 3D representation, respectively. The localization circuitry determines the right side of the body in the CT images, based on a comparison result for the first and second number of voxels. The localization circuitry detects a first bottom portion of right lung based on a distribution of intensity values of pixels in a region of right lung. The localization circuitry detects a second bottom portion of the rib region and localizes the liver organ in the CT images, from a reference of the detected first bottom portion and the detected second bottom portion.

Abstract: An assistive apparatus for organ localization, includes storing a 3D representation and CT images of an anatomical portion of the body of a subject. A localization circuitry determines a rib region and a spine region in the CT images and calculates first and second number of voxels within a first and second region of the 3D representation, respectively. The localization circuitry determines the right side of the body in the CT images, based on a comparison result for the first and second number of voxels. The localization circuitry detects a first bottom portion of right lung based on a distribution of intensity values of pixels in a region of right lung. The localization circuitry detects a second bottom portion of the rib region and localizes the liver organ in the CT images, from a reference of the detected first bottom portion and the detected second bottom portion.

Abstract: A method for automatic organ segmentation in CT images comprises in a first step, rough region segmentation; in a second step, coarse organ segmentation; and in a third step, refinement of organ segmentation. The organ may be a liver. Rough region segmentation may comprise applying standard anatomical knowledge to the CT images. Coarse segmentation may comprise identifying organ voxels using a probabilistic model. Refinement of organ segmentation may comprise refinement based on intensity, followed by refinement based on shape. Apparatuses configured to carry out the method are also disclosed.

Abstract: Various aspects of a system and a method to provide assistance in a surgery in presence of tissue deformation are disclosed herein. In accordance with an embodiment, the system includes an electronic device that receives one or more tissue material properties of a plurality of surface structures of an anatomical portion. One or more boundary conditions associated with the anatomical portion may also be received. Surface displacement of the anatomical portion may be determined by matching a first surface of the anatomical portion before deformation with a corresponding second surface of the anatomical portion after the deformation. The volume displacement field of the anatomical portion may be computed based on the determined surface displacement, the received one or more tissue material properties, and the received one or more boundary conditions.

Abstract: A fully automatic brain tumor segmentation and classification method and system improve the healthcare experience with machine intelligence. The automatic brain tumor segmentation and classification method and system utilize whole tumor segmentation and multi-class tumor segmentation to provide accurate analysis.

Abstract: A fully automatic brain tumor segmentation and classification method and system improve the healthcare experience with machine intelligence. The automatic brain tumor segmentation and classification method and system utilize whole tumor segmentation and multi-class tumor segmentation to provide accurate analysis.

Abstract: A fully automatic brain tumor segmentation and classification method and system improve the healthcare experience with machine intelligence. The automatic brain tumor segmentation and classification method and system utilize whole tumor segmentation and multi-class tumor segmentation to provide accurate analysis.

Abstract: A fully automatic brain tumor segmentation and classification method and system improve the healthcare experience with machine intelligence. The automatic brain tumor segmentation and classification method and system utilize whole tumor segmentation and multi-class tumor segmentation to provide accurate analysis.

Abstract: System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.

Abstract: Various aspects of a method and a system for segmentation of vascular structure in a volumetric image dataset are disclosed herein. The method comprises initial segmentation of an input volumetric image dataset to obtain a main vessel structure and a plurality of broken segments. A weighted path is computed between the main vessel structure and a broken segment of the plurality of broken segments. The computation of the weighted path is based on at least one or more parameters associated with a first voxel of the broken segment and a second voxel of the main vessel structure. A valid, weighted path is determined between the main vessel structure and a broken segment of the plurality of broken segments, based on the computed weighted path and one or more pre-specified conditions. Based on the determined valid, weighted path, an output volumetric image dataset is generated by performance of a final segmentation on a gradient field.

Abstract: Various aspects of a system and a method to provide assistance in a surgery in presence of tissue deformation are disclosed herein. In accordance with an embodiment, the system includes an electronic device that receives one or more tissue material properties of a plurality of surface structures of an anatomical portion. One or more boundary conditions associated with the anatomical portion may also be received. Surface displacement of the anatomical portion may be determined by matching a first surface of the anatomical portion before deformation with a corresponding second surface of the anatomical portion after the deformation. The volume displacement field of the anatomical portion may be computed based on the determined surface displacement, the received one or more tissue material properties, and the received one or more boundary conditions.

Abstract: Various aspects of a method and a system for segmentation of vascular structure in a volumetric image dataset are disclosed herein. The method comprises initial segmentation of an input volumetric image dataset to obtain a main vessel structure and a plurality of broken segments. A weighted path is computed between the main vessel structure and a broken segment of the plurality of broken segments. The computation of the weighted path is based on at least one or more parameters associated with a first voxel of the broken segment and a second voxel of the main vessel structure. A valid, weighted path is determined between the main vessel structure and a broken segment of the plurality of broken segments, based on the computed weighted path and one or more pre-specified conditions. Based on the determined valid, weighted path, an output volumetric image dataset is generated by performance of a final segmentation on a gradient field.

Abstract: An apparatus and method for performing automatic 3D image segmentation and reconstruction of organ structures, which is particularly well-suited for use on cortical surfaces is presented. A brain extraction process removes non-brain image elements, then classifies brain tissue as to type in preparation for a cerebrum segmentation process that determines which portions of the image information belong to specific physiological structures. Ventricle filling is performed on the image data based on information from a ventricle extraction process. A reconstruction process follows in which specific surfaces, such as white matter (WM) and grey matter (GM), are reconstructed.

Abstract: An apparatus and method for performing automatic 3D image segmentation and reconstruction of organ structures, which is particularly well-suited for use on cortical surfaces is presented. A brain extraction process removes non-brain image elements, then classifies brain tissue as to type in preparation for a cerebrum segmentation process that determines which portions of the image information belong to specific physiological structures. Ventricle filling is performed on the image data based on information from a ventricle extraction process. A reconstruction process follows in which specific surfaces, such as white matter (WM) and grey matter (GM), are reconstructed.