Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep Learning Article Swipe

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Sanket Badhe , Varun Pratap Singh , Joy Li , Paras Lakhani ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2001.01277

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.

Related Topics

Segmentation Fault

Deep Learning

Radiography

Artificial Intelligence

Convolutional Neural Network

Concepts

Sørensen–Dice coefficient Segmentation Deep learning Radiography Artificial intelligence Convolutional neural network Dice Computer science Medicine Pattern recognition (psychology) Image segmentation Radiology Mathematics Geometry

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2001.01277
PDF: https://arxiv.org/pdf/2001.01277
OA Status: green
Cited By: 2
References: 13
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2001.01277

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Title: Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep Learning

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-01-05

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Authors: Sanket Badhe, Varun Pratap Singh, Joy Li, Paras Lakhani

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Landing page: https://arxiv.org/abs/2001.01277

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Concepts: Sørensen–Dice coefficient, Segmentation, Deep learning, Radiography, Artificial intelligence, Convolutional neural network, Dice, Computer science, Medicine, Pattern recognition (psychology), Image segmentation, Radiology, Mathematics, Geometry

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Cited by: 2

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References (count): 13

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