Year: 2022
Type: preprint
Abstract: Objective: Thigh muscle group segmentation is important for assessment of muscle anatomy, metabolic disease and aging. Many efforts have been put into quantifying muscle tissues with magnetic resonance (MR) imaging including manual annotation of individual muscles. However, leveraging publicly available annotations in MR images to achieve muscle group segmentation on single slice computed tomography (CT) thigh images is challenging. Method: We propose an unsupervised domain adaptation pipeline with self-training to transfer labels from 3D MR to single CT slice. First, we transform the image appearance from MR to CT with CycleGAN and feed the synthesized CT images to a segmenter simultaneously. Single CT slices are divided into hard and easy cohorts based on the entropy of pseudo labels inferenced by the segmenter. After refining easy cohort pseudo labels based on anatomical assumption, self-training with easy and hard splits is applied to fine tune the segmenter. Results: On 152 withheld single CT thigh images, the proposed pipeline achieved a mean Dice of 0.888(0.041) across all muscle groups including sartorius, hamstrings, quadriceps femoris and gracilis. muscles Conclusion: To our best knowledge, this is the first pipeline to achieve thigh imaging domain adaptation from MR to CT. The proposed pipeline is effective and robust in extracting muscle groups on 2D single slice CT thigh images.The container is available for public use at https://github.com/MASILab/DA_CT_muscle_seg
Source: arXiv (Cornell University)
Institution: Vanderbilt University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, Vanderbilt Institute for Clinical and Translational Research, National Institutes of Health, National Institute on Aging, National Institute of General Medical Sciences, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, National Center for Advancing Translational Sciences
Awards: G1094848838, G1486488711, CAREER: Modeling Personalized Brain Development with Big Data, G2116968943, G2665080673, NSF Convergence Accelerator Track D: Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for Accelerated Impact (STRAIT I3), G3377571803, Medical Scientist Training Program, G4418762578, G5090652903, G5575560717, G6160673999, G64116031, The Vanderbilt Institute for Clinical and Translational Research (VICTR) UL1, G6671297155, Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRI, G7412434281, The Vanderbilt Institute for Clinical and Translational Research (VICTR), G8679799969, G9000035503, Postdoctoral Training in Biomedical MRI and MRS, Vanderbilt Institute for Clinical and Translational Research (VICTR)
OpenAlex ID: W4310627707