Brain tumor image segmentation is a challenging research topic in which
deep-learning models have presented the best results. However, the traditional
way of training those models from many pre-annotated images leaves several
unanswered questions. Hence methodologies, such as Feature Learning from Image
Markers (FLIM), have involved an expert in the learning loop to reduce human
effort in data annotation and build models sufficiently deep for a given
problem. FLIM has been successfully used to create encoders, estimating the
filters of all convolutional layers from patches centered at marker voxels. In
this work, we present Multi-Step (MS) FLIM - a user-assisted approach to
estimating and selecting the most relevant filters from multiple FLIM
executions. MS-FLIM is used only for the first convolutional layer, and the
results already indicate improvement over FLIM. For evaluation, we build a
simple U-shaped encoder-decoder network, named sU-Net, for glioblastoma
segmentation using T1Gd and FLAIR MRI scans, varying the encoder's training
method, using FLIM, MS-FLIM, and backpropagation algorithm. Also, we compared
these sU-Nets with two State-Of-The-Art (SOTA) deep-learning models using two
datasets. The results show that the sU-Net based on MS-FLIM outperforms the
other training methods and achieves effectiveness within the standard
deviations of the SOTA models.

脑肿瘤图像分割是一个具有挑战性的研究课题，深度学习模型呈现出最佳结果。然而，传统的从许多预注释图像中训练这些模型的方式还存在一些未解答的问题。因此，方法学，例如从图像标记学习特征 (FLIM)，已经在学习循环中涉及专家，以减少数据注释的人力工作量，并在一个给定的问题上构建足够深的模型。FLIM 已成功用于创建编码器，估计从标记像素为中心的补丁的所有卷积层的滤波器。在这项工作中，我们提出了多步骤 (MS) FLIM - 一种用户辅助的方法，用于从多个 FLIM 执行中估计和选择最相关的滤波器。MS-FLIM 仅用于第一层卷积层，结果已经表明相对于 FLIM 存在改进。为了评估，我们构建了一个简单的 U 形编码器 - 解码器网络，名为 sU-Net，用于胶质母细胞瘤分割，使用 T1Gd 和 FLAIR MRI 扫描，改变编码器的训练方法，使用 FLIM，MS-FLIM 和反向传播算法。此外，我们使用两个数据集将这些 sU-Net 与两个最先进的深度学习模型进行了比较。结果显示，基于 MS-FLIM 的 sU-Net 胜过其他训练方法，并在 SOTA 模型的标准偏差范围内实现了有效性。