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Abstract #1400

Thyroid nodule segmentation on dynamic contrast-enhanced magnetic resonance imaging based on Spatial -Temporal information fusion

Binze Han1,2, Qian Yang3, Meini Wu3, Kexin Chen1,2, Wenming Deng3, Wei Cui4, Dehong Luo3, Dong Liang2,5, Hairong Zheng2,5, Qian Wan2, Zhou Liu3, and Na Zhang2,5
1Southern University of Science and Technology (SUSTech), shenzhen, China, 2Paul C. Lauterbur Research Center for BiomedicalImaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, 3Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 113 Baohe Avenue, 518116, shenzhen, China, 4GE Healthcare, MR Research China, Beijing, China, 5Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, shenzhen, China

Synopsis

Keywords: Analysis/Processing, Segmentation, Thyroid gland,DCE-MRI

Motivation: Automatic segmentation of thyroid nodules on DCE-MRI is vital for improved characterization and diagnosis. Traditional segmentation methods often rely on a static phase , sacrificing valuable spatio-temporal information inherent in DCE-MRI.

Goal(s): This study aimed to develop a deep-learning model that integrates spatial and temporal pharmacokinetic information in DCE-MRI for improved segmentation.

Approach: We designed a novel deep-learning workflow that integrates spatial features and temporal pharmacokinetic information from DCE-MRI, accounting for individual differences in contrast enhancement and morphology, to overcome the limitations of methods focusing solely on spatial information.

Results: Our model outperformed eight classic segmentation networks, confirming its effectiveness in thyroid nodule segmentation.

Impact: The success of our model may inspire further research into advanced deep-learning architectures that harness individual intensity variations, morphological priors, and temporal pharmacokinetic information. This general approach could extend beyond DCE-MRI to encompass other medical imaging modalities containing temporal information.

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Keywords