Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
Amal Rannen TrikiChulmin Joo

Abstract

Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. This work explores automated margin assessment on a sample of patient data collected at the Pathology Department, Severance Hospital (Seoul, South Korea). Some methods based on the spatial statistics of the images have been developed, but the obtained results are still far from human performance. In this work, we investigate the possibility to use deep neural networks (DNNs) for real time margin assessment, demonstrating performance significantly better than the reported literature and close to the level of a human expert. Since the goal is to detect the presence of cancer, a patch-based classification method is proposed, as it is sufficient for detection, and requires training data that is easier and cheaper to collect than for other approaches such as segmentation. For that purpose, we train a DNN architecture that was proved to be efficient for small images on patches e...Continue Reading

Citations

Jan 3, 2019·Journal of Pathology and Translational Medicine·Hye Yoon ChangTae-Yeong Kwak

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