Journal article
Segmentation quality assessment by automated detection of erroneous surface regions in medical images
Computers in biology and medicine, Vol.164, 107324
09/2023
DOI: 10.1016/j.compbiomed.2023.107324
PMCID: PMC10563140
PMID: 37591161
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 0.95 and 0.92, and the average positive predictive values of 0.78 and 0.91 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.
Details
- Title: Subtitle
- Segmentation quality assessment by automated detection of erroneous surface regions in medical images
- Creators
- Fahim Ahmed Zaman - University of IowaLichun Zhang - University of IowaHonghai Zhang - University of IowaMilan Sonka - University of IowaXiaodong Wu - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Computers in biology and medicine, Vol.164, 107324
- DOI
- 10.1016/j.compbiomed.2023.107324
- PMID
- 37591161
- PMCID
- PMC10563140
- NLM abbreviation
- Comput Biol Med
- ISSN
- 0010-4825
- eISSN
- 1879-0534
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health; DOI: 10.13039/100000016, name: U.S. Department of Health and Human Services; DOI: 10.13039/100000070, name: National Institute of Biomedical Imaging and Bioengineering, award: R01-EB004640
- Language
- English
- Electronic publication date
- 08/2023
- Date published
- 09/2023
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Iowa Technology Institute; Radiation Oncology; The Iowa Institute for Biomedical Imaging; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984449860202771
Metrics
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