Recent advancements in deep learning have led to the possibility of increased performance in computer vision tools. A major development has been the usage of Convolutional Neural Networks (CNN) for automatically detecting features within a given image. Architectures such as YOLO1 have obtained incredibly high performances for the real-time detection of every-day objects within images. However to date, there have been few reports of deep learning applied to detect anatomical features within CT scans; especially those within the cardiovascular space. We propose here an automatic anatomical feature detection pipeline for identifying the features of the left atrium using a CNN. Slices of CT scans were fed into a single neural network which predicted the four bounding box coordinates that encapsulate the left atrium. The network can be optimized end-to-end and generate predictions at great speed, achieving a validation smooth L1 loss of 11.95 when predicting the left atrial bounding boxes.
A Deep Learning Approach for the Automatic Identification of the Left Atrium Within CT Scans
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Deakyne, A, Gaasedelen, E, & Iaizzo, PA. "A Deep Learning Approach for the Automatic Identification of the Left Atrium Within CT Scans." Proceedings of the 2019 Design of Medical Devices Conference. 2019 Design of Medical Devices Conference. Minneapolis, Minnesota, USA. April 15–18, 2019. V001T01A005. ASME. https://doi.org/10.1115/DMD2019-3282
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