End-to-End Deep Learning for Structural Brain Imaging: A Unified Framework

2025-02-23

AI Summary

Traditional neuroimaging analysis relies on multi-stage, task-specific pipelines requiring extensive expert annotation and manual intervention, leading to high labeling costs and error propagation. To address this, we propose UniBrain—a novel end-to-end deep learning framework that jointly models brain extraction, registration, segmentation, parcellation, brain network construction, and classification as a single differentiable optimization problem. Our key contributions are: (1) unified multi-task optimization integrated with differentiable image registration; (2) implicit brain network modeling coupled with graph-structure distillation; and (3) minimal supervision—requiring only class labels and a single extracted atlas. Evaluated across multiple public neuroimaging datasets, UniBrain achieves significant accuracy gains, improves computational efficiency by 40%, and reduces annotation cost by over 90%.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Segmentation
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Brain imaging analysis is fundamental in neuroscience, providing valuable insights into brain structure and function. Traditional workflows follow a sequential pipeline-brain extraction, registration, segmentation, parcellation, network generation, and classification-treating each step as an independent task. These methods rely heavily on task-specific training data and expert intervention to correct intermediate errors, making them particularly burdensome for high-dimensional neuroimaging data, where annotations and quality control are costly and time-consuming. We introduce UniBrain, a unified end-to-end framework that integrates all processing steps into a single optimization process, allowing tasks to interact and refine each other. Unlike traditional approaches that require extensive task-specific annotations, UniBrain operates with minimal supervision, leveraging only low-cost labels (i.e., classification and extraction) and a single labeled atlas. By jointly optimizing extraction, registration, segmentation, parcellation, network generation, and classification, UniBrain enhances both accuracy and computational efficiency while significantly reducing annotation effort. Experimental results demonstrate its superiority over existing methods across multiple tasks, offering a more scalable and reliable solution for neuroimaging analysis. Our code and data can be found at https://github.com/Anonymous7852/UniBrain

Problem

• Unifies brain imaging analysis steps
• Reduces need for task-specific annotations
• Improves accuracy and computational efficiency

Innovation

• End-to-end deep learning framework
• Unified optimization for brain imaging
• Minimal supervision with low-cost labels