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Proceedings Paper

Pre-trained deep convolutional neural networks for the segmentation of malignant pleural mesothelioma tumor on CT scans
Author(s): Eyjolfur Gudmundsson; Christopher M. Straus; Samuel G. Armato III
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Paper Abstract

Pre-trained deep convolutional neural networks (CNNs) have shown promise in the training of deep CNNs for medical imaging applications. The purpose of this study was to investigate the use of partially pre-trained deep CNNs for the segmentation of malignant pleural mesothelioma tumor on CT scans. Four network configurations were investigated: (1) VGG16/U-Net network with pre-trained layers fixed during training, (2) VGG16/U-Net network with pre-trained layers fine-tuned during training, (3) VGG16/U-Net network with all except the first two pre-trained layers fine-tuned during training, and (4) a standard U-Net architecture trained from scratch. Deep CNNs were trained separately for tumor segmentation in left and right hemithoraces using 4259 and 6441 contoured axial CT sections, respectively. A test set of 61 CT sections from 16 patients excluded from training was used to evaluate segmentation performance; the Dice similarity coefficient (DSC) was calculated between computer-generated and reference segmentations provided by two radiologists and one radiology resident. Median DSC on the test set was 0.739 (range 0.328–0.920), 0.772 (range 0.342–0.949), 0.777 (range 0.216–0.946), and 0.758 (range 0.099–0.943) across all observers for network configurations (1), (2), (3) and (4) above, respectively. The median DSC achieved with configuration (3) when compared with the standard U-Net trained from scratch was found to be significantly higher for two out of three observers. A fine-tuned VGG16/U-Net deep CNN showed significantly higher overlap with two out of three observers when compared with a standard U-Net trained from scratch for the segmentation of malignant pleural mesothelioma tumor.

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503J (13 March 2019); doi: 10.1117/12.2512974
Show Author Affiliations
Eyjolfur Gudmundsson, The Univ. of Chicago (United States)
Christopher M. Straus, The Univ. of Chicago (United States)
Samuel G. Armato III, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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