Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep Learning

• URL: http://arxiv.org/abs/2001.01277v1
• Date: Sun, 5 Jan 2020 17:35:04 GMT
• Title: Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep Learning
• Authors: Sanket Badhe, Varun Singh, Joy Li and Paras Lakhani
• Abstract summary: The purpose of this study is to develop an automated algorithm for vertebral segmentation on chest radiography using deep learning. A U-Net deep convolutional neural network was employed for segmentation, using the sum of dice coefficient and binary cross-entropy as the loss function. On the test set, the algorithm demonstrated an average dice coefficient value of 90.5 and an average intersection-over-union (IoU) of 81.75.
• Score: 0.19116784879310023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The purpose of this study is to develop an automated algorithm for thoracic vertebral segmentation on chest radiography using deep learning. 124 de-identified lateral chest radiographs on unique patients were obtained. Segmentations of visible vertebrae were manually performed by a medical student and verified by a board-certified radiologist. 74 images were used for training, 10 for validation, and 40 were held out for testing. A U-Net deep convolutional neural network was employed for segmentation, using the sum of dice coefficient and binary cross-entropy as the loss function. On the test set, the algorithm demonstrated an average dice coefficient value of 90.5 and an average intersection-over-union (IoU) of 81.75. Deep learning demonstrates promise in the segmentation of vertebrae on lateral chest radiography.

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