Ramot at Tel-aviv University Ltd.

Tel Hashomer Medical Research Infrastructure and Services Ltd.

Other

Bar-ilan University

Eli Konen

Arnaldo Mayer

Nahum Kiryati

Yitzhak Avital

Ori Weber

Hayit Greenspan

Jacob Goldberger

Michael Green

Edith M Marom

Moshe Hadani

Uri Avni

Michal Sharon

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. The intermediate outputs are inputted into a single common decoding-expanding component for computing the indication of segmented 3D ROI(s).

Systems and methods for the segmentation of multi-modal image data

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.

Method and system for providing a locally-consistent enhancement of a low-quality image

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, MRI, of the target site to a preoperative magnetic resonance image, iMRI, of the target site acquired by an interoperative iMRI scanner, and a non-rigid body transform that transforms the iMRIimage to an image iMRIimage 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 MRIimage.

High resolution intraoperative MRI images

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.

Method and system of classifying medical images

Growing community of inventors

Tel Aviv, Israel

Eli Konen