NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI

2026-09-15

AI Summary

为解决儿童脑肿瘤在多模态MRI中的精确分割问题，提出NeuroTS-Net模型，采用三维编码解码卷积神经网络架构，实现更优的自动分割效果。

Abstract

Pediatric brain tumors are a leading cause of cancer-related mortality in children, and their small, rare, and often low-contrast subregions make accurate manual delineation challenging. Reliable automated segmentation is therefore needed to support diagnosis, treatment planning, and response assessment. Accordingly, we introduce NeuroTS-Net, a three-dimensional encoder-decoder convolutional neural network architecture for multi-class semantic segmentation that incorporates a dual-scale raw-detail stream, adaptive low-resolution context selection, and detail-preserving multipath downsampling. These components preserve fine intensity and boundary information while efficiently modeling broader tumor context. NeuroTS-Net was trained on the BraTS 2026 pediatric dataset without external data or pretrained weights and evaluated against nnU-Net and MedNeXt under the same experimental protocol. NeuroTS-Net outperformed the baseline methods, achieving whole-tumor and tumor-core Dice scores of 0.938 and 0.937 on the internal validation set and 0.927 and 0.926 on the official challenge validation set. The code is open-sourced at: https://github.com/maenstru56/NeuroTS.

Problem

• Pediatric brain tumors
• semantic segmentation
• multi-modal MRI
• automated segmentation
• diagnosis

Innovation

• three-dimensional encoder-decoder
• dual-scale raw-detail stream
• adaptive low-resolution context selection
• detail-preserving multipath downsampling