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XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets
Author(s): Joseph Bullock; Carolina Cuesta-Lázaro; Arnau Quera-Bofarull
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Paper Abstract

X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions. We apply a machine learning approach to this problem, presenting an end-to-end solution which results in robust and efficient inference. Since medical institutions often do not have the resources to process and label the large quantity of X-Ray images usually needed for neural network training, we design an end-to-end solution for small datasets, while achieving state-of-the-art results. Our implementation produces an overall accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical image processing techniques, such as clustering and entropy based methods, while improving upon the output of existing neural networks used for segmentation in non-medical contexts. The code used for this project is available online.1

Paper Details

Date Published: 15 March 2019
PDF: 11 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531Z (15 March 2019); doi: 10.1117/12.2512451
Show Author Affiliations
Joseph Bullock, Durham Univ. (United Kingdom)
Carolina Cuesta-Lázaro, Durham Univ. (United Kingdom)
Arnau Quera-Bofarull, Durham Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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