Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep Learning

Autor:	Badhe, Sanket, Singh, Varun, Li, Joy, Lakhani, Paras
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages, Accepted Poster presentation at Conference on Machine Intelligence in Medical Imaging 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.01277 Zobrazit plný text záznamu View this record from Arxiv