Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation.

Yue Sun, Kun Gao, Sijie Niu, Weili Lin, Gang Li, Li Wang
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Abstract

To characterize early cerebellum development, accurate segmentation of the cerebellum into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) tissues is one of the most pivotal steps. However, due to the weak tissue contrast, extremely folded tiny structures, and severe partial volume effect, infant cerebellum tissue segmentation is especially challenging, and the manual labels are hard to obtain and correct for learning-based methods. To the best of our knowledge, there is no work on the cerebellum segmentation for infant subjects less than 24 months of age. In this work, we develop a semi-supervised transfer learning framework guided by a confidence map for tissue segmentation of cerebellum MR images from 24-month-old to 6-month-old infants. Note that only 24-month-old subjects have reliable manual labels for training, due to their high tissue contrast. Through the proposed semi-supervised transfer learning, the labels from 24-month-old subjects are gradually propagated to the 18-, 12-, and 6-month-old subjects, which have a low tissue contrast. Comparison with the state-of-the-art methods demonstrates the superior performance of the proposed method, especially for 6-month-old subjects.

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婴儿小脑组织分割的半监督迁移学习。
为了描述早期小脑的发育特征,将小脑准确地划分为白质(WM)、灰质(GM)和脑脊液(CSF)组织是最关键的步骤之一。然而,由于组织对比度较弱,微小结构极度折叠,局部体积效应严重,婴儿小脑组织分割尤其具有挑战性,基于学习的方法难以获得和校正人工标记。据我们所知,目前还没有针对小于24个月的婴儿进行小脑分割的研究。在这项工作中,我们开发了一个半监督迁移学习框架,该框架由一个置信度图指导,用于对24个月至6个月婴儿的小脑MR图像进行组织分割。请注意,只有24个月大的受试者有可靠的手动训练标签,因为他们的组织对比度高。通过本文提出的半监督迁移学习,将24月龄被试的标签逐渐传播到组织对比度较低的18、12、6月龄被试。与最先进的方法比较表明,所提出的方法具有优越的性能,特别是对于6个月大的受试者。
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