XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets

Joseph Bullock; Carolina Cuesta-Lázaro; Arnau Quera-Bofarull

doi:10.1117/12.2512451

15 March 2019 XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets

Joseph Bullock, Carolina Cuesta-Lázaro, Arnau Quera-Bofarull

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109531Z (2019) https://doi.org/10.1117/12.2512451
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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

Citation Download Citation

Joseph Bullock, Carolina Cuesta-Lázaro, and Arnau Quera-Bofarull "XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531Z (15 March 2019); https://doi.org/10.1117/12.2512451

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