bibtext: @InProceedings{ Hu_ConsecutiveContrastive_MICCAI2024,
                   author = { Hu, Dan and Han, Kangfu and Cheng, Jiale and Li, Gang },
                   title = { { Consecutive-Contrastive Spherical U-net: Enhancing Reliability of Individualized Functional Brain Parcellation for Short-duration fMRI Scans } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15002 },
                   month = {October},
                   pages = { pending },
                }
abstract: Individualized brain parcellations derived from functional MRI (fMRI) are essential for discerning unique functional patterns of individuals, facilitat-ing personalized diagnoses and treatments. Unfortunately, as fMRI signals are inherently noisy, establishing reliable individualized parcellations typ-ically necessitates long-duration fMRI scan (> 25 min), posing a major chal-lenge and resulting in the exclusion of numerous short-duration fMRI scans from individualized studies. To address this issue, we develop a novel Consecutive-Contrastive Spherical U-net (CC-SUnet) to enable the predic-tion of reliable individualized brain parcellation using short-duration fMRI data, greatly expanding its practical applicability. Specifically, 1) the wide-ly used functional diffusion map (DM), obtained from functional connec-tivity, is carefully selected as the predictive feature, for its advantage in tracing the transitions between regions while reducing noise. To ensure a robust depiction of brain network, we propose a dual-task model to predict DM and cortical parcellation simultaneously, fully utilizing their reciprocal relationship. 2) By constructing a stepwise dataset to capture the gradual changes of DM over increasing scan durations, a consecutive prediction framework is designed to realize the prediction from short-to-long gradual-ly. 3) A stepwise-denoising-prediction module is further proposed. The noise representations are separated and replaced by the latent representa-tions of a group-level diffusion map, realizing informative guidance and de-noising concurrently. 4) Additionally, an N-pair contrastive loss is intro-duced to strengthen the discriminability of the individualized parcella-tions. Extensive experimental results demonstrated the superiority of our proposed CC-SUnet in enhancing the reliability of the individualized par-cellation with short-duration fMRI data, thereby significantly boosting their utility in individualized studies.
title: Consecutive-Contrastive Spherical U-net: Enhancing Reliability of Individualized Functional Brain Parcellation for Short-duration fMRI Scans
authors: Hu, Dan Han, Kangfu Cheng, Jiale Li, Gang