Abstract: There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output.

Abstract: There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output.

Abstract: There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output.

Abstract: There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output.

Abstract: A method of providing a medical image of a ROI of a patient, the method comprising: acquiring a first medical image of a region of interest (ROI) of a patient, the medical image characterized by a first signal to noise ratio (SNR); determining for a given pixel in the first image a plurality of different first image patches in the first image, each having a pixel that is coincident with the given pixel; determining for each first image patch a similar second image patch having a second SNR greater than the first SNR; determining an enhanced pixel value for the given pixel having an enhanced SNR greater than the first SNR responsive to pixel values of pixels in the determined second image patches; and using the determined pixel value to generate a second medical image of the ROI having an enhanced SNR greater than the first SNR.

Abstract: A method of providing a medical image of a ROI of a patient, the method comprising: acquiring a first medical image of a region of interest (ROI) of a patient, the medical image characterized hy a first signal to noise ratio (SNR); determining for a given pixel in the first image a plurality of different first image patches in the first image, each having a pixel that is coincident with the given pixel; determining for each first image patch a similar second image patch having a second SNR greater than the first SNR; determining an enhanced pixel value for the given pixel having an enhanced SNR greater than the first SNR responsive to pixel values of pixels in the determined second image patches; and using the determined pixel value to generate a second medical image of the ROI having an enhanced SNR greater than the first SNR.

Abstract: A method of providing an intraoperative magnetic resonance image of a target site of a patient body at which a medical procedure is performed comprising determining a rigid body transform that transforms a high resolution preoperative magnetic resonance image, MRIo, of the target site to a preoperative magnetic resonance image, iMRIo, of the target site acquired by an interoperative iMRI scanner, and a non-rigid body transform that transforms the iMRIo image to an image iMRI1 image of the site acquired by the interoperative iMRI scanner during the medical procedure, and using the rigid and non-rigid body transforms to transform the high resolution MRIo image.

Abstract: A method for estimating a presence of a pneumothorax abnormality. The method comprises classifying at least one texture feature of each of a plurality of pixels of a chest radiograph (CXR) image to generate an output map, identifying at least one lung contour in said CXR image, identifying a plurality of multiple pixel segments along said at least one lung contour, combining values of pixels in each one of said plurality of multiple pixel segments from said output map to generate a global descriptor for said CXR image, and estimating a presence of said pneumothorax abnormality in said CXR image by applying a statistical classifier on said global descriptor.

Abstract: A method of providing an intraoperative magnetic resonance image of a target site of a patient body at which a medical procedure is performed, the method comprising: acquiring a high resolution preoperative magnetic resonance image (MRI), MRI0, of a first region of the patient comprising the target site, the MRI0 image comprising a plurality of slices MRI0 n having voxels; acquiring a preoperative, iMRI0 image of a second region of the patient comprising the target site, using an iMRI scanner having a field of view (FOV), the iMRI0 image comprising plurality of slices iMRI0 m having voxels; registering the MRI0 image to the iMRI0 image to provide a rigid body transform (RT0) that transforms the MRI0 to the iMRI0 image; acquiring an IMRI1 image of the target site during performance of the procedure; registering the image iMRI0 to the iMRIj image to obtain a non-rigid body transform (NRT); and applying RT0 and NRT to MRI0 to provide a high resolution (hiQ-iMRIj) image.

Abstract: A method of tracking a region in a video image having a plurality of video frames is disclosed. The method comprises: generating one or more candidate contours in a video frame; and, for each candidate contour, analyzing the candidate contour based on intensity values of picture-elements along the candidate contour, and analyzing an area at least partially enclosed by the candidate contour based on texture features in the area. The method further comprises selecting a winner contour from the candidate contour(s) based on the analyses, and associating the region with the winner contour.

Abstract: A method of generating a category model for classifying medical images. The method comprises providing a plurality of medical images each categorized as one of a plurality of categorized groups, generating an index of a plurality of visual words according to a distribution of a plurality of local descriptors in each the image, modeling a category model mapping a relation between each visual word and at least one of the categorized groups according to the index, and outputting the category model for facilitating the categorization of an image based on local descriptors thereof.

Abstract: A method of generating a category model for classifying medical images. The method comprises providing a plurality of medical images each categorized as one of a plurality of categorized groups, generating an index of a plurality of visual words according to a distribution of a plurality of local descriptors in each the image, modeling a category model mapping a relation between each visual word and at least one of the categorized groups according to the index, and outputting the category model for facilitating the categorization of an image based on local descriptors thereof.

Abstract: A method of generating a category model for classifying medical images. The method comprises providing a plurality of medical images each categorized as one of a plurality of categorized groups, generating an index of a plurality of visual words according to a distribution of a plurality of local descriptors in each the image, modeling a category model mapping a relation between each visual word and at least one of the categorized groups according to the index, and outputting the category model for facilitating the categorization of an image based on local descriptors thereof.