Paper
6 March 2023 Building brain tumor segmentation networks with user-assisted filter estimation and selection
Matheus A. Cerqueira, Flávia Sprenger, Bernardo C. A. Teixeira, Alexandre X. Falcão
Author Affiliations +
Proceedings Volume 12567, 18th International Symposium on Medical Information Processing and Analysis; 125670O (2023) https://doi.org/10.1117/12.2669770
Event: 18th International Symposium on Medical Information Processing and Analysis, 2022, Valparaíso, Chile
Abstract
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.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Matheus A. Cerqueira, Flávia Sprenger, Bernardo C. A. Teixeira, and Alexandre X. Falcão "Building brain tumor segmentation networks with user-assisted filter estimation and selection", Proc. SPIE 12567, 18th International Symposium on Medical Information Processing and Analysis, 125670O (6 March 2023); https://doi.org/10.1117/12.2669770
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KEYWORDS
Image segmentation

Tumors

Brain

Image filtering

Machine learning

Magnetic resonance imaging

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