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首页> 外文会议>International Workshop on Machine Learning in Medical Imaging;International Conference on Medical Image Computing and Computer-Assisted Intervention >Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation
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Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation

机译：婴幼儿细胞组织分割的半监督转移学习

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摘要

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.
机译：为了表征早期小脑开发，将小脑细胞的精确分割成白质（WM），灰质（GM）和脑脊液（CSF）组织是最枢转的步骤之一。然而，由于组织对比弱，极其折叠的微小结构，严重的部分体积效应，婴儿小脑组织分割尤其具有挑战性，并且手动标签很难获得并校正基于学习的方法。据我们所知，婴儿受试者的小脑细分没有工作，不到24个月。在这项工作中，我们开发了一个半监督的转移学习框架，以便从24个月龄至6个月大的婴幼儿从小脑细胞图像的组织分割引导。请注意，由于其高组织对比，只有24个月的历史科目具有可靠的手动标签进行培训。通过提出的半监督转移学习，来自24个月历史的受试者的标签逐渐繁殖到18个，12-和6个月的历史科目，其具有低组织对比。与最先进的方法的比较表明了该方法的优越性，特别是对于6个月大的科目。

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