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4 April 2022 Spatial label smoothing via aleatoric uncertainty for bleeding region segmentation from laparoscopic videos
Jie Qiu, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Nobuyoshi Takeshita, Masaaki Ito, Kensaku Mori
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Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120320U (2022) https://doi.org/10.1117/12.2611672
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
Abstract
Automatically segmenting bleeding regions is an essential task for computer-assisted surgery systems. Recent development in deep learning has boosted the performance of medical image segmentation. However, training deep neural networks, in general, requires high-quality pixel-wise annotations, in which such an annotating process is expensive and easy to introduce annotation noise. To address this issue, motivated by the observation that the noise should be data-dependent, we propose an uncertainty-guided label smoothing method instead of using a fixed label smoothing strategy. Aleatoric uncertainty, which accounts for inherent noise such as annotation error, is estimated via an additional branch of deep neural networks. With the help of estimated aleatoric uncertainty, we could guide the spatial label smoothing in a self-adaptive manner. We demonstrated the effectiveness of our proposal by evaluating the Dice coefficient in a private bleeding segmentation dataset. An improvement over the baseline was observed for our proposal.
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Jie Qiu, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Nobuyoshi Takeshita, Masaaki Ito, and Kensaku Mori "Spatial label smoothing via aleatoric uncertainty for bleeding region segmentation from laparoscopic videos", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120320U (4 April 2022); https://doi.org/10.1117/12.2611672
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KEYWORDS
Image segmentation
Laparoscopy
Image processing
Medical imaging
Neural networks
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