发表机构
Lucian Blaga University of Sibiu; University of Arizona; Pediatric Clinical Hospital of Sibiu; Johns Hopkins University; MedLife Polisano Hospital（锡比乌卢奇安·布拉加大学; 亚利桑那大学; 锡比乌儿科临床医院; 约翰斯·霍普金斯大学; MedLife Polisano医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对儿童脑肿瘤分割难题，提出NeuroTS-Net三维编解码卷积网络，融合双尺度细节流与自适应上下文，在BraTS 2026数据集上超越nnU-Net等基线，实现高精度多类分割。
AI 中文摘要
儿童脑肿瘤是儿童癌症相关死亡的主要原因，其亚区体积小、罕见且常呈低对比度，使得准确的手动勾画具有挑战性。因此，需要可靠的自动分割来支持诊断、治疗规划和疗效评估。为此，我们提出了NeuroTS-Net，一种用于多类语义分割的三维编码器-解码器卷积神经网络架构，该架构融合了双尺度原始细节流、自适应低分辨率上下文选择以及细节保持的多路径下采样。这些组件在有效建模更广泛的肿瘤上下文的同时，保留了精细的强度和边界信息。NeuroTS-Net在BraTS 2026儿科数据集上进行了训练，未使用外部数据或预训练权重，并在相同实验协议下与nnU-Net和MedNeXt进行了评估。NeuroTS-Net优于基线方法，在内部验证集上实现了全肿瘤和肿瘤核心的Dice分数分别为0.938和0.937，在官方挑战验证集上分别为0.927和0.926。代码已在以下网址开源：此https URL。
英文摘要
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.
CommentsAccepted at the 2026 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) - BraTS Cluster of Challenges: Pediatric Brain Tumor Segmentation (BraTS-PEDs)
