Facilitated By

San Antonio Medical Foundation


Southwest Research Institute

Southwest Research Institute (SwRI), headquartered in San Antonio, Texas, is one of the oldest and largest independent, nonprofit, applied research and development (R&D) organizations in the United States.

Principal Investigator(s)
Courtney Rouse
Funded by
Research Start Date

Chromosome instability (CIN) and aneuploidy are classic hallmarks of cancer, caused by cells deviating from the normal cell cycle. In a tumor, CIN is variable from cell to cell and can result in many forms of aneuploidy, including changes in chromosome structure, number, and arrangement. Polyploidy is a form of aneuploidy that results in several nuclei within a single cell. Polyploidy has been found in numerous cancers and is related to therapeutic resistance. Currently, there is no effective method to identifying polyploid cells due to the high degree of similarity between them and surrounding cancer cells. Automating the identification of polyploid cells using Artificial Intelligence (AI) could aid drug development and research efforts targeting polyploidy.

A custom neural network was trained to identify polyploidy in tissue images. A detection algorithm produces a set of bounding boxes corresponding to each detected polyploid cell, after which duplicate/overlapping boxes are eliminated, and then feature vectors are sampled from an earlier layer of the neural network. These features are fed into a second convolutional neural network that predicts which pixels are part of that polyploid cell. Images of mouse liver tissue that contain polyploid cells were obtained from Dr. Daruka Mahadevan’s lab at the University of Texas Health Science Center of San Antonio (UTHSCSA). With assistance from hematopathologists at UTHSCSA, polyploid cells in 30 images were labeled as 4n-, 8n-, or cellular polyploidy, and used as input data to train the custom machine learning algorithm. The dataset was iterated over for 300 epochs to solve for the model parameters. The trained algorithm was tested on 25 new images from the same set of mouse liver data. The testing labels were corrected, and the training process was repeated with the 25 new images combined with the original 30 training images. The retrained algorithm was then tested on 32 new images.

Collaborative Project
Basic Research
Medical Devices
Behavioral Health