Optimizing Machine Learning Algorithms for Multiclass Neuroimaging Segmentation
ISEF · 2022 Robotics and Intelligent Machines
Overview
In recent years, analysis of cerebral structures using whole-brain quantitative neuroimaging has been widely recognized for its ability to provide deep insights on neurological disease. Quantifying morphological and volumetric changes in MRI holds vast potential to revolutionize diagnostics for neurological pathologies like multiple sclerosis, epilepsy, dementia, glioblastoma, and traumatic brain injury. The segmentation of 3D MRI for gray-matter, white-matter, cerebrospinal fluid, lesions, and key neural structures can thus provide crucial diagnostic information to neurologists. Although manual or semi-automatic quantification by radiologists have been the historical norm, they have proven too time-consuming for widespread clinical implementation. Working with a dataset of 30 scans, this study tested the efficacies of various machine learning approaches in performing whole-brain multi-tissue quantification through classifier segmentation. Utilizing an approach focused on optimizing the feature "stack" of processed variations of input images used in training, experimentation showed that a combination of gaussian blur subtraction, laplacian smoothing, minimums-based sampling, and structure tensors yielded a stack which—when paired with normalization preprocessing and an optimized random forest algorithm—yielded near 98% accuracy in quantifying every tissue visible in FLAIR MRI. Although limited by hardware constraints and data availability, the study’s novel understanding of the relationship between gaussian differences and minimums-based voxel sampling proved—when implemented—to yield increased effectiveness in distinguishing between brain matter despite contrast irregularities—providing a framework for future brain quantification algorithm development.
Competition history
- ISEF 2022
Resources
Related projects
ISEF · 2019
Segmenting CT Slices: Optimizing Lesion Detection through Mask Region-based Convolutional Neural Networks
ISEF · 2021
Determining the Optimal MRI Sequence for the Automatic Segmentation of Multiple Sclerosis Using Convolutional Encoder Networks
ISEF · 2023
Brain Tumor Segmentation and Classification Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Predictions
ISEF · 2024
Volumetric Segmentation and Multimodal Classification of Brain Tumors Using Point Sampling and 3D CNNs
Closest projects by meaning, across every fair and year in the corpus.
Source: Regeneron International Science and Engineering Fair