International Business Machines Corporation

Hui Tang

Mehdi Moradi

Tanveer Fathima Syeda-Mahmood

Hongzhi Wang

Chun Lok Wong

Ahmed El Harouni

Gopalkrishna Veni

Prasanth Prasanna

A method comprises (a) collecting (i) a set of chest computed tomography angiography (CTA) images scanned in the axial view and (ii) a manual segmentation of the images, for each one of multiple organs; (b) preprocessing the images such that they share the same field of view (FOV); (c) using both the images and their manual segmentation to train a supervised deep learning segmentation network, wherein loss is determined from a multi-dice score that is the summation of the dice scores for all the multiple organs, each dice score being computed as the similarity between the manual segmentation and the output of the network for one of the organs; (d) testing a given (input) pre-processed image on the trained network, thereby obtaining segmented output of the given image; and (e) smoothing the segmented output of the given image.

System for segmentation of anatomical structures in cardiac CTA using fully convolutional neural networks

Methods for image segmentation and smoothing of image segmentations are provided. In various embodiments, a plurality of training images is provided to a segmentation network. A candidate segmentation is obtained from the segmentation network for each of the plurality of training images. Each candidate segmentation is compared to a ground truth segmentation to compute a loss metric for each candidate segmentation. Based on the gradient of the loss, the segmentation network is trained to minimize level set smoothing energy. In various embodiments, an input image is downsampled from a first resolution to a second, lower resolution. The downsampled image is provided to a segmentation network. A segmentation at the second resolution is obtained from the segmentation network. The segmentation from the second resolution is upsampled to the first resolution. The input image and the upsampled segmentation are provided at the first resolution to a convolutional network. An updated segmentation at the first resolution is obtained therefrom.

Framework for integrating deformable modeling with 3D deep neural network segmentation

Automatic characterization of the Agatston score from coronary computed tomography (CT) is provided. In various embodiments, a plurality of coronary computed tomography images are segmented into a plurality of segments corresponding to features of coronary anatomy. A plurality of calcium candidates are extracted from the plurality of coronary computed tomography images by thresholding. Coronary calcification is located in the coronary computed tomography images by applying a trained classifier to the plurality of calcium candidates. An Agatson score is computed from the located calcification.

Automatic characterization of Agatston score from coronary computed tomography

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Hui Tang