Although many studies have successfully applied transfer learning to medical
image segmentation, very few of them have investigated the selection strategy
when multiple source tasks are available for transfer. In this paper, we
propose a prior knowledge guided and transferability based framework to select
the best source tasks among a collection of brain image segmentation tasks, to
improve the transfer learning performance on the given target task. The
framework consists of modality analysis, RoI (region of interest) analysis, and
transferability estimation, such that the source task selection can be refined
step by step. Specifically, we adapt the state-of-the-art analytical
transferability estimation metrics to medical image segmentation tasks and
further show that their performance can be significantly boosted by filtering
candidate source tasks based on modality and RoI characteristics. Our
experiments on brain matter, brain tumor, and white matter hyperintensities
segmentation datasets reveal that transferring from different tasks under the
same modality is often more successful than transferring from the same task
under different modalities. Furthermore, within the same modality, transferring
from the source task that has stronger RoI shape similarity with the target
task can significantly improve the final transfer performance. And such
similarity can be captured using the Structural Similarity index in the label
space.

本文提出了一种基于先验知识和可转移性的框架来选择最好的源任务，在收集到的多个脑图像分割任务中进行选择，以提高给定目标任务的转移学习性能。在脑物质、脑肿瘤和白质高信号异常分割数据集上进行的实验表明，从相同模态下的不同任务转移通常比从不同模态下的同一任务转移更成功。此外，在相同模态下，从具有与目标任务更强的 RoI 形状相似性的源任务转移可以显着提高最终的转移性能，并且可以使用标签空间中的结构相似度指数来捕获这种相似性。