News

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. 

Sat, 2016-08-27 16:03
New patent awarded to CCIPD
Saturday, August 27, 2016 - 16:03

CCIPD awarded new US patent (9,424,460) on methods, apparatus, and other embodiments associated with predicting prostate cancer (CaP) progression using tumor cell morphology features and benign region graph features

 
Inventors: Anant Madabhushi, George Lee, Sahir Ali
 

Methods, apparatus, and other embodiments associated with predicting prostate cancer (CaP) progression using tumor cell morphology features and benign region graph features are described. 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. The first region may concern cancerous cells and the second region may concern benign cells.

 

 

 

Wed, 2016-08-17 13:00
Dr Satish Viswanath is now an Assistant Professor of Biomedical Engineering
Wednesday, August 17, 2016 - 13:00

Dr Satish Viswanath is now an Assistant Professor of Biomedical Engineering at Case Western Reserve University School of Medicine. He will be setting his laboratory with a focus on developing image informatic tools for interventions, at Wolstein Research Building.

Fri, 2016-04-01 11:02
5 CCIPD abstracts accepted at ASCO
Friday, April 1, 2016 - 11:02

ASCO - American Society of Clinical Oncology is the biggest oncology meeting in the US. CCIPD gets to showcase its work on computational imaging and precision medicine at this year's meeting with 5 abstracts being accepted. 

Congrats to Nate, Sagar, George, Ania, Niha, Mehdi, Mahdi for having their abstracts accepted as posters.

Congratulations also to all of our clinical collaborators at Case, University Hospitals, CCF, NIH and Johns Hopkins.

Our abstracts spanned our work on breast, prostate, and lung cancer.