Automated brain tumor segmentation has significant importance, especially for disease diagnosis and treatment planning. The study utilizes a range of MRI modalities, namely T1-weighted (T1), T1-contrast-enhanced (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR), with each providing unique and vital information for accurate tumor localization. While state-of-the-art models perform well on standardized datasets like the BraTS dataset, their suitability in diverse clinical settings (matrix size, slice thickness, manufacturer-related differences such as repetition time, and echo time) remains a subject of debate. This research aims to address this gap by introducing a novel ‘Region-Focused Selection Plus (RFS+)’ strategy designed to efficiently improve the generalization and quantification capabilities of deep learning (DL) models for automatic brain tumor segmentation. RFS+ advocates a targeted approach, focusing on one region at a time. It presents a holistic strategy that maximizes the benefits of various segmentation methods by customizing input masks, activation functions, loss functions, and normalization techniques. Upon identifying the top three models for each specific region in the training dataset, RFS+ employs a weighted ensemble learning technique to mitigate the limitations inherent in each segmentation approach. In this study, we explore three distinct approaches, namely, multi-class, multi-label, and binary class for brain tumor segmentation, coupled with various normalization techniques applied to individual sub-regions. The combination of different approaches with diverse normalization techniques is also investigated. A comparative analysis is conducted among three U-net model variants, including the state-of-the-art models that emerged victorious in the BraTS 2020 and 2021 challenges. These models are evaluated using the dice similarity coefficient (DSC) score on the 2021 BraTS validation dataset. The 2D U-net model yielded DSC scores of 77.45%, 82.14%, and 90.82% for enhancing tumor (ET), tumor core (TC), and the whole tumor (WT), respectively. Furthermore, on our local dataset, the 2D U-net model augmented with the RFS+ strategy demonstrates superior performance compared to the state-of-the-art model, achieving the highest DSC score of 79.22% for gross tumor volume (GTV). The model utilizing RFS+ requires 10% less training dataset, 67% less memory and completes training in 92% less time compared to the state-of-the-art model. These results confirm the effectiveness of the RFS+ strategy for enhancing the generalizability of DL models in brain tumor segmentation.
自动脑肿瘤分割具有重要价值，尤其在疾病诊断与治疗规划领域。本研究采用多模态磁共振成像技术，包括T1加权成像（T1）、对比增强T1加权成像（T1ce）、T2加权成像（T2）以及液体衰减反转恢复序列（FLAIR），每种模态都能为肿瘤精确定位提供独特且关键的影像信息。尽管当前先进模型在BraTS等标准化数据集上表现优异，但其在不同临床场景（如矩阵尺寸、层厚、厂商相关的重复时间与回波时间差异）中的适用性仍存争议。本研究通过提出创新的"区域聚焦选择增强（RFS+）"策略，旨在有效提升深度学习模型在脑肿瘤自动分割中的泛化能力与量化性能。RFS+倡导针对性策略，每次聚焦单一区域，通过定制化输入掩膜、激活函数、损失函数及归一化技术，形成最大化各类分割方法优势的整体方案。在训练集中确定各区域最优的三种模型后，RFS+采用加权集成学习技术以克服单一分割方法的固有局限。本研究探索了多类别、多标签及二分类三种脑肿瘤分割方法，并结合针对各子区域的不同归一化技术，同时考察了多种方法与归一化技术的组合效果。通过对包括BraTS 2020和2021挑战赛优胜模型在内的三种U-net变体进行对比分析，在2021年BraTS验证集上采用戴斯相似系数（DSC）评估模型性能。二维U-net模型在增强肿瘤（ET）、肿瘤核心（TC）及全肿瘤（WT）区域分别获得77.45%、82.14%和90.82%的DSC评分。此外，在本土数据集上，采用RFS+策略增强的二维U-net模型相较于先进模型展现出更优性能，在大体肿瘤体积（GTV）分割中获得79.22%的最高DSC评分。该模型训练数据需求减少10%，内存消耗降低67%，训练时间缩短92%。这些结果证实了RFS+策略在提升脑肿瘤分割深度学习模型泛化能力方面的有效性。
肿瘤(癌症)患者之家