  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.
