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.

翻译：脑肿瘤图像分割是一个具有挑战性的研究课题，其中深度学习模型已取得了最佳效果。然而，传统上通过大量预标注图像训练这些模型的方式仍存在若干未解决的问题。因此，诸如从图像标记中学习特征（FLIM）等方法将专家纳入学习循环中，以减少数据标注所需的人力，并为特定问题构建足够深的模型。FLIM已成功用于构建编码器，通过从标记体素中心的图像块估算所有卷积层的滤波器。在本工作中，我们提出多步FLIM（MS-FLIM）——一种通过多次FLIM执行来估算并选择最相关滤波器的用户辅助方法。MS-FLIM仅用于第一卷积层，而结果已显示出相对于FLIM的改进。为进行评估，我们构建了一个简单的U型编码器-解码器网络（命名为sU-Net），用于使用T1Gd和FLAIR MRI扫描进行胶质母细胞瘤分割，通过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与两种最先进的深度学习模型进行了比较。结果表明，基于MS-FLIM的sU-Net在性能上优于其他训练方法，并在标准差的范围内达到了与最先进模型相当的效果。