Anant Madabhushi, the F. Alex Nason professor II of biomedical engineering and director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD), and his team were issued three patents in digital pathology and precision medicine.
U.S. patent 9,424,460 titled “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 c
- 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 George Lee and Sahirzeeshan Ali.
U.S. patent 9,430,829 titled “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, second and third probabilities.
Co-inventors include Haibo Wang and Angel Cruz Roa.
U.S. patent 9,430,830 titled “Spatially aware Cell Cluster (SpACCl) Graphs for Quantitative Histomorphometry” describes the methods, apparatus and other embodiments associated with objectively predicting disease aggressiveness using 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.
The co-inventor is Ali.