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Segmentation Quality Assessment by Automated Detection of Erroneous Surface Regions in Medical Images
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  • Fahim Ahmed Zaman ,
  • Lichun Zhang ,
  • Honghai Zhang ,
  • Milan Sonka ,
  • Xiaodong Wu
Fahim Ahmed Zaman
University of Iowa, University of Iowa

Corresponding Author:[email protected]

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Lichun Zhang
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Honghai Zhang
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Milan Sonka
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Xiaodong Wu
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Despite the advancement in deep learning-based semantic segmentation methods, which have achieved accuracy levels of field experts in many computer vision applications, the same general approaches may frequently fail in 3D medical image segmentation due to complex tissue structures, noisy acquisition, disease-related pathologies, as well as the lack of sufficiently large datasets with associated annotations. For expeditious diagnosis and quantitative image analysis in large-scale clinical trials, there is a compelling need to predict segmentation quality without ground truth. In this paper, we propose a deep learning framework to locate erroneous regions on the boundary surfaces of segmented objects for quality control and assessment of segmentation. A Convolutional Neural Network (CNN) is explored to learn the boundary related image features of multi-objects that can be used to identify location-specific inaccurate segmentation. The predicted error locations can facilitate efficient user interaction for interactive image segmentation (IIS). We evaluated the proposed method on two data sets: Osteoarthritis Initiative (OAI) 3D knee MRI and 3D calf muscle MRI. The average sensitivity scores of $0.96\pm0.00$ and $0.96\pm0.02$, and the average positive predictive values of $0.87\pm0.01$ and $0.93\pm0.03$ were achieved, respectively, for erroneous surface region detection of knee cartilage segmentation and calf muscle segmentation. Our experiment demonstrated promising performance of the proposed method for segmentation quality assessment by automated detection of erroneous surface regions in medical images.
Sep 2023Published in Computers in Biology and Medicine volume 164 on pages 107324. 10.1016/j.compbiomed.2023.107324