Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning
| Autor: | Jung, Sunyoung, Choi, Yoonseok, Al-masni, Mohammed A., Jung, Minyoung, Kim, Dong-Hyun |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Medical Image Computing and Computer Assisted Intervention MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15009. Springer, Cham |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/978-3-031-72114-4_21 |
| Popis: | Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making segmentation difficult. This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation in the presence of artifacts. The core concept lies in a complementary process: a disentanglement learning network progressively removes artifacts, leading to cleaner images and consequently, more accurate segmentation by a jointly trained motion estimation and segmentation network. This network generates three outputs: a motioncorrected image, a motion deformation map that identifies artifact-affected regions, and a brain tissue segmentation mask. This deformation serves as a guidance mechanism for the disentanglement process, aiding the model in recovering lost information or removing artificial structures introduced by the artifacts. Extensive in-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans. Comment: Medical Image Computing and Computer Assisted Intervention, MICCAI 2024 |
| Databáze: | arXiv |
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