期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 40, 期 6, 页码 1702-1710出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2021.3064661
关键词
Image segmentation; Strain; Convolution; Biomedical imaging; Computational efficiency; Training data; Training; Expected label value (ELV); supervised image segmentation; soft segmentation; atlas; MRI; CT
类别
资金
- National Institutes of Health (NIH)
- National Institute of Diabetes and Digestive and Kidney Diseases [K01DK101631]
- National Institute on Aging [R56AG068261, R01AG022381, R01AG016495]
- National Institute for Biomedical Imaging and Bioengineering [P41EB015896, R01EB019956, R01EB023281]
- National Institute for Neurological Disorders and Stroke [R01NS105820, R01NS083534, U01NS086625]
- BrightFocus Foundation [A2016172S]
- NIH Shared Instrumentation [S10RR023401, S10RR019307, S10RR023043, S10RR028832]
- O2 High Performance Compute Cluster at Harvard Medical School
- Enterprise Research Infrastructure and Services at Mass General Brigham (MGB)
- AWS Cloud Credits for Research program
A new approach for medical image segmentation is proposed, which calculates the probability of all possible atlas-to-image transformations and the expected label value (ELV), avoiding the issue of local optima. This method does not require actually performing deformable registration, hence saving computational costs.
The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible atlas-to-image transformations and compute the expected label value (ELV), thereby not relying merely on the transformation deemed optimal by the registration method. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We evaluate our ELV computation approach by applying it to brain, liver, and pancreas segmentation on datasets of magnetic resonance and computed tomography images.
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