Segmentation Quality Assessment by Automated Detection of Erroneous
Surface Regions in Medical Images
Abstract
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.