Center for Computational Imaging and Personalized Diagnostics researchers awarded patents
On June 23, two patents were awarded to inventors from the Center for Computational Imaging and Personalized Diagnostics (CCIPD). The patents are titled “Intra-Perinodular Textural Transition (IPRIS): A Three Dimensional (3D) Descriptor for Nodule Diagnosis on Lung Computed Tomography (CT) Images” and “Treatment planning and evaluation for rectal cancer via image analytics.”
“Treatment planning and evaluation for rectal cancer via image analytics”
United States Serial Number (USSN): 10,692,607
Inventors: Satish Viswanath, assistant professor of biomedical engineering; Anant Madabhushi, the F. Alex Nason Professor II of Biomedical Engineering; and Jacob Antunes, graduate student researcher
Methods and apparatus associated with predicting colorectal cancer tumor invasiveness are described. One example apparatus includes a set of circuits, and a data store that stores radiological images of tissue demonstrating colorectal cancer. The set of circuits includes a circumferential resection margin (CRM) prediction circuit that generates a CRM probability score for a diagnostic radiological image, an image acquisition circuit that acquires a diagnostic radiological image of a region of tissue demonstrating colorectal cancer pathology and that provides the diagnostic radiological image to the CRM prediction circuit, and a training circuit that trains the CRM prediction circuit to quantify chemoradiation response in the region of tissue represented in the diagnostic radiological image. The training circuit trains the CRM prediction circuit using a set of composite images.
“Intra-Perinodular Textural Transition (IPRIS): A Three Dimensional (3D) Descriptor for Nodule Diagnosis on Lung Computed Tomography (CT) Images”
United States Serial Number (USSN): 10,692,211
Inventors: Mehdi Alilou, research associate; and Anant Madabhushi, the F. Alex Nason Professor II of Biomedical Engineering
Embodiments classify lung nodules by accessing a 3D radiological image of a region of tissue, the 3D image including a plurality of voxels and slices, a slice having a thickness; segmenting the nodule represented in the 3D image across contiguous slices, the nodule having a 3D volume and 3D interface, where the 3D interface includes an interface voxel; partitioning the 3D interface into a plurality of nested shells, a nested shell including a plurality of 2D slices, a 2D slice including a boundary pixel; extracting a set of intra-perinodular textural transition (Ipris) features from the 2D slices based on a normal of a boundary pixel of the 2D slices; providing the Ipris features to a machine learning classifier which computes a probability that the nodule is malignant, based, at least in part, on the set of Ipris features; and generating a classification of the nodule based on the probability.
(From The Daily, 7/31/2020)