A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images |
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| Institution: | 1. College of Computer Science, Sichuan University, Chengdu 610065, China;2. Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong;3. College of Biomedical Engineering, Sichuan University, Chengdu 610065, China;4. Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong;5. Tencent AI Lab, Shenzhen 518057, China;1. IBM Zurich Research Lab, Zurich, Switzerland;2. Computer-Assisted Applications in Medicine, ETH Zurich, Zurich, Switzerland;1. Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, 250358, China;2. School of Information and Computer, Taiyuan University of Technology, Shanxi, 030000, China;3. Department of Medical Imaging, Western University, London, ON, Canada;4. Digital Imaging Group of London, London, ON, Canada;1. Tencent AI Lab, Shenzhen, Guangdong 518057, China;2. Perception and Robotics Group, University of Maryland, College Park, MD 20742, USA;3. Zhejiang University, Hangzhou, Zhejiang 310027, China;4. Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA;1. Physical Sciences, Sunnybrook Research Institute, Toronto, Canada;2. Department of Medical Biophysics, University of Toronto, Canada |
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| Abstract: | Hepatocellular carcinoma (HCC), as the most common type of primary malignant liver cancer, has become a leading cause of cancer deaths in recent years. Accurate segmentation of HCC lesions is critical for tumor load assessment, surgery planning, and postoperative examination. As the appearance of HCC lesions varies greatly across patients, traditional manual segmentation is a very tedious and time-consuming process, the accuracy of which is also difficult to ensure. Therefore, a fully automated and reliable HCC segmentation system is in high demand. In this work, we present a novel hybrid neural network based on multi-task learning and ensemble learning techniques for accurate HCC segmentation of hematoxylin and eosin (H&E)-stained whole slide images (WSIs). First, three task-specific branches are integrated to enlarge the feature space, based on which the network is able to learn more general features and thus reduce the risk of overfitting. Second, an ensemble learning scheme is leveraged to perform feature aggregation, in which selective kernel modules (SKMs) and spatial and channel-wise squeeze-and-excitation modules (scSEMs) are adopted for capturing the features from different spaces and scales. Our proposed method achieves state-of-the-art performance on three publicly available datasets, with segmentation accuracies of 0.797, 0.923, and 0.765 in the PAIP, CRAG, and UHCMC&CWRU datasets, respectively, which demonstrates its effectiveness in addressing the HCC segmentation problem. To the best of our knowledge, this is also the first work on the pixel-wise HCC segmentation of H&E-stained WSIs. |
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