Abstract: The present disclosure, in some embodiments, relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including accessing an imaging data set having one or more radiological images of a patient having Crohn's disease, the one or more radiological images including one or more intestinal strictures; identifying a plurality of determinative features from within the one or more intestinal strictures in the one or more radiological images, the plurality of determinative features being associated with one or more pathological features used to identify inflammation or fibrosis within an intestinal stricture; and applying a machine learning model to the plurality of determinative features to identify an extent of inflammation or fibrosis within the one or more intestinal strictures.

Abstract: The present disclosure, in some embodiments, relates to a non-transitory computer-readable medium storing computer-executable instructions. The computer readable medium is configured to cause a processor to access an image volume of a rectum comprising a rectal tumor. A forward mapping is generated based on non-rigidly registering a healthy rectal atlas to the image volume. The forward mapping is inverted to generate an inverse mapping from the image volume to the healthy rectal atlas. Based on the inverse mapping, a plurality of deformation vectors, associated with a deformation within a rectal wall of the rectum, are determined. Magnitude based deformation features and orientation based deformation features are computed from the plurality of deformation vectors. One or more of the magnitude based deformation features and one or more of the orientation based deformation features are utilized to determine a response of a patient to a chemoradiation treatment.

Abstract: In some embodiments, the present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including forming an imaging dataset having imaging data corresponding to one or more radiological images of a patient having a bowel disease; operating upon the imaging data with one or more convolutional neural network (CNN) segments configured to generate a plurality of CNN outputs, the one or more CNN segments respectively including a convolution layer configured to perform a convolution on the imaging data; and applying a wavelet network to the plurality of CNN outputs to generate a plurality of convolution wavelet network (CWN) outputs, the wavelet network being configured to decompose the plurality of CNN outputs according to a mother wavelet. A predictive signature associated with disease response or risk is constructed using the plurality of CWN outputs.

Abstract: The present disclosure, in some embodiments, relates to a non-transitory computer-readable medium storing computer-executable instructions. The computer readable medium is configured to cause a processor to access an image volume of a rectum comprising a rectal tumor. A forward mapping is generated based on non-rigidly registering a healthy rectal atlas to the image volume. The forward mapping is inverted to generate an inverse mapping from the image volume to the healthy rectal atlas. Based on the inverse mapping, a plurality of deformation vectors, associated with a deformation within a rectal wall of the rectum, are determined. Magnitude based deformation features and orientation based deformation features are computed from the plurality of deformation vectors. One or more of the magnitude based deformation features and one or more of the orientation based deformation features are utilized to determine a response of a patient to a chemoradiation treatment.

Abstract: Embodiments discussed herein facilitate determination of responsiveness to chemoradiation treatment in rectal cancer patients based on structural deformation features obtained from a pre- or post-treatment medical imaging. One example embodiment can perform operations comprising: accessing an image volume of a rectum comprising a rectal tumor; generating a forward mapping based on non-rigidly registering a healthy rectal atlas to the image volume; inverting the forward mapping to generate an inverse mapping from the image volume to the healthy rectal atlas; determining, based on the inverse mapping, an associated deformation magnitude for each voxel of a plurality of voxels associated with the rectum; computing one or more structural deformation features based on the associated deformation magnitudes for the plurality of voxels; and predicting via a classifier whether or not the rectal tumor will respond to the chemoradiation treatment based at least in part on the one or more structural deformation features.

Abstract: Embodiments discussed herein facilitate determination of responsiveness to chemoradiation treatment in rectal cancer patients based on structural deformation features obtained from a pre- or post-treatment medical imaging. One example embodiment can perform operations comprising: accessing an image volume of a rectum comprising a rectal tumor; generating a forward mapping based on non-rigidly registering a healthy rectal atlas to the image volume; inverting the forward mapping to generate an inverse mapping from the image volume to the healthy rectal atlas; determining, based on the inverse mapping, an associated deformation magnitude for each voxel of a plurality of voxels associated with the rectum; computing one or more structural deformation features based on the associated deformation magnitudes for the plurality of voxels; and predicting via a classifier whether or not the rectal tumor will respond to the chemoradiation treatment based at least in part on the one or more structural deformation features.