News

Mon, 2016-10-03 16:14
Andrew Janowczyk awarded a 2 year NIDDK fellowship
Monday, October 3, 2016 - 16:14
Andrew Janowczyk has been awarded a 2 year NIDDK (National Institute for Digestive Diseases and Kidney) T32 fellowship for $100K. As part of this project Andrew will be working on computational interrogation of renal biopsies to identify which patients will have stable versus progressive disease.

 

Thu, 2016-09-15 16:11
New paper in American Journal of Neuroradiology
Thursday, September 15, 2016 - 16:11

The BrIC Lab published a new paper differentiating radiation injury from brain tumor without biopsy

Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study

P. Tiwari, P. Prasanna, L. Wolansky, M. Pinho, M. Cohen, A.P. Nayate, A. Gupta, G. Singh, K. Hattanpaa, A. Sloan, L. Rogers, and A. Madabhushi

AJNR Am J Neuroradiol first published on 15 September 2016

doi:10.3174/ajnr.A4931

Link to paper here

Thu, 2016-09-15 16:07
CCIPD in the news
Thursday, September 15, 2016 - 16:07

CCIPD in the news this week. Two articles covering breast cancer and brain cancer. 

Read "New computer program beatsphysicians at brain cancer diagnosis" here

Read "Computerized Tissue Image Analysis sheds light on underlying genomics of ER+ breast cancer" here

Thu, 2016-09-01 16:05
Three patents awarded to CCIPD in a week
Thursday, September 1, 2016 - 16:05

U.S. patent 9,424,460 entitled "Tumor plus adjacent benign signature (TABS) for quantitative histomorphometry" describes methods, apparatus, and other embodiments associated with predicting prostate cancer (CaP) progression using tumor cell morphology features and benign region graph features . One example apparatus includes a set of logics that acquires an image of a region of tissue, detects and segments cells in the image, extracts a set of morphological features from cells in a first region in the image, constructs a graph of a localized cellular network in a second region of the image, extracts a set of graph features from the graph, generates a set of tumor plus adjacent features signature (TABS) features from the sets of graph features and the set of morphological features, and calculates the probability that the image is a progressor or non-progressor based, at least in part, on the set of TABS features. 

 

Co-inventors include Dr. George Lee and Mr. Sahirzeeshan Ali.

U.S. patent 9,430,829 entitled "Automatic Detection Of Mitosis Using Handcrafted And Convolutional Neural Network Features" describes the apparatus associated with detecting mitosis in breast cancer pathology images by combining handcrafted (HC) and convolutional neural network (CNN) features in a cascaded architecture. The approach includes a set of logics that acquires an image of a region of tissue, partitions the image into candidate patches, generates a first probability that the patch is mitotic using an HC feature set and a second probability that the patch is mitotic using a CNN-learned feature set, and classifies the patch based on the first probability and the second probability. If the first and second probabilities do not agree, the apparatus trains a cascaded classifier on the CNN-learned feature set and the HC feature set, generates a third probability that the patch is mitotic, and classifies the patch based on a weighted average of the first probability, the second probability, and the third probability.

 

Co-inventors include Drs. Haibo Wang and Angel Cruz Roa.

U.S. patent 9,430,830 entitled "Spatially aware Cell Cluster (SpACCl) Graphs for Quantitative Histomorphometry" describes the methods, apparatus, and other embodiments associated with objectively predicting disease aggressiveness using Spatially Aware Cell Cluster (SpACCl) graphs. One example apparatus includes a set of logics that acquires an image of a region of tissue, partitions the image into a stromal compartment and an epithelial compartment, identifies cluster nodes within the compartments, constructs a spatially aware stromal sub-graph and a spatially aware epithelial sub-graph based on the cluster nodes and a probabilistic decaying function of the distance between cluster nodes, extracts local features from the sub-graphs, and predicts the aggressiveness of a disease in the region of tissue based on the sub-graphs and the extracted features. 

 

Co-inventor is Mr. Sahirzeeshan Ali.