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Proceedings Paper

Improving splenomegaly segmentation by learning from heterogeneous multi-source labels
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Paper Abstract

Splenomegaly segmentation on computed tomography (CT) abdomen anatomical scans is essential for identifying spleen biomarkers and has applications for quantitative assessment in patients with liver and spleen disease. Deep convolutional neural network automated segmentation has shown promising performance for splenomegaly segmentation. However, manual labeling of abdominal structures is resource intensive, so the labeled abdominal imaging data are rare resources despite their essential role in algorithm training. Hence, the number of annotated labels (e.g., spleen only) are typically limited with a single study. However, with the development of data sharing techniques, more and more publicly available labeled cohorts are available from different resources. A key new challenging is to co-learn from the multi-source data, even with different numbers of labeled abdominal organs in each study. Thus, it is appealing to design a co-learning strategy to train a deep network from heterogeneously labeled scans. In this paper, we propose a new deep convolutional neural network (DCNN) based method that integrates heterogeneous multi-resource labeled cohorts for splenomegaly segmentation. To enable the proposed approach, a novel loss function is introduced based on the Dice similarity coefficient to adaptively learn multi-organ information from different resources. Three cohorts were employed in our experiments, the first cohort (98 CT scans) has only splenomegaly labels, while the second training cohort (100 CT scans) has 15 distinct anatomical labels with normal spleens. A separate, independent cohort consisting of 19 splenomegaly CT scans with labeled spleen was used as testing cohort. The proposed method achieved the highest median Dice similarity coefficient value (0.94), which is superior (p-value<0.01 against each other method) to the baselines of multi-atlas segmentation (0.86), SS-Net segmentation with only spleen labels (0.90) and U-Net segmentation with multi-organ training (0.91). Our approach for adapting the loss function and training structure is not specific to the abdominal context and may be beneficial in other situations where datasets with varied label sets are available.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094908 (15 March 2019); doi: 10.1117/12.2512842
Show Author Affiliations
Yucheng Tang, Vanderbilt Univ. (United States)
Yuankai Huo, Vanderbilt Univ. (United States)
Yunxi Xiong, Vanderbilt Univ. (United States)
Hyeonsoo Moon, Vanderbilt Univ. (United States)
Albert Assad, Incyte Corp. (United States)
Tamara K. Moyo, Vanderbilt Univ. Medical Ctr. (United States)
Michael R. Savona, Vanderbilt Univ. Medical Ctr. (United States)
Richard Abramson, Vanderbilt Univ. Medical Ctr. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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