Cedars-sinai Medical Center

Georgia State University Research Foundation, Inc.

Arkadiusz Gertych

Beatrice Knudsen

Yi Jiang

Sergey Klimov

Embodiments of the present systems and methods may provide improved capability to predict the risk of recurrence of ductal carcinoma in situ (DCIS) conditions using whole slide image analysis based on machine learning techniques. For example, in an embodiment, a computer-implemented method for determining treatment of a patient may comprise receiving an image of living tissue of a patient, annotating the entire image into tissue structures, extracting texture features from the annotated image, determining a distribution of the extracted texture features relative to tissue conditions, classifying the patient into a risk group based on the distribution, and treating the patient accordingly based on the risk group.

Predicting DCIS recurrence risk using a machine learning-based high-content image analysis approach

In various embodiments, the invention teaches systems and methods for analyzing an image of cells, including an image obtained by confocal microscopy, and delineating cell nuclei in the image. In some embodiments, the systems and methods apply a three-dimensional (3-D) radial symmetry transform followed by adaptive post processing of symmetry images to arrive at a mask of seeds that can guide watershed-based segmentation.

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El Segundo, CA, United States of America

Arkadiusz Gertych