统一HT-CNNs架构：用于分割MRI中从胶质瘤到儿童肿瘤等多种脑肿瘤的迁移学习
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors
AI总结：
本文提出统一迁移学习架构HT-CNNs，通过集成混合Transformer与卷积神经网络并结合迁移学习，在多种脑肿瘤MRI分割任务上超越BraTS以往获胜方法，为医学图像分割及临床应用提供新方案。
摘要：
从3D多模态MRI中准确分割脑肿瘤，对多种脑肿瘤的诊断和治疗规划至关重要。本文应对BraTS 2023（脑肿瘤分割挑战赛2023）提出的挑战，提出了一种适用于更广泛脑肿瘤类型的统一迁移学习方法。我们提出了HT-CNNs（混合Transformer与卷积神经网络），这是一种通过迁移学习优化的混合Transformer与卷积神经网络集成模型，用于多种脑肿瘤分割。该方法可从MRI数据中捕获空间和上下文细节，并在代表常见肿瘤类型的多样化数据集上进行微调。通过迁移学习，HT-CNNs利用从一个任务中学到的表征来提升在另一个任务中的泛化能力，借助在大型数据集上预训练的模型的能力，并针对特定肿瘤类型进行微调。我们对来自多个国际来源的多样化数据集进行了预处理，确保其能代表最常见的脑肿瘤类型。我们的严格评估在所有肿瘤类型上采用标准化的定量指标，确保了鲁棒性和泛化性。所提出的集成模型在BraTS验证数据集上取得了优于以往获胜方法的分割结果。使用DSC（戴斯相似系数）和HD95（95%豪斯多夫距离）的综合定量评估证明了我们方法的有效性。定性的分割预测进一步验证了我们模型生成的高质量输出。我们的研究结果凸显了迁移学习和集成方法在医学图像分割中的潜力，表明其能显著提升临床决策和患者护理水平。尽管面临后处理和域偏移相关的挑战，我们的研究为脑肿瘤分割的未来研究树立了新的标杆。代码和模型的Docker镜像已公开发布，地址为https://hub.docker.com/r/razeineldin/ht-cnns。
英文摘要：
Accurate segmentation of brain tumors from 3D multimodal MRI is vital for diagnosis and treatment planning across diverse brain tumors. This paper addresses the challenges posed by the BraTS 2023, presenting a unified transfer learning approach that applies to a broader spectrum of brain tumors. We introduce HT-CNNs, an ensemble of Hybrid Transformers and Convolutional Neural Networks optimized through transfer learning for varied brain tumor segmentation. This method captures spatial and contextual details from MRI data, fine-tuned on diverse datasets representing common tumor types. Through transfer learning, HT-CNNs utilize the learned representations from one task to improve generalization in another, harnessing the power of pre-trained models on large datasets and fine-tuning them on specific tumor types. We preprocess diverse datasets from multiple international distributions, ensuring representativeness for the most common brain tumors. Our rigorous evaluation employs standardized quantitative metrics across all tumor types, ensuring robustness and generalizability. The proposed ensemble model achieves superior segmentation results across the BraTS validation datasets over the previous winning methods. Comprehensive quantitative evaluations using the DSC and HD95 demonstrate the effectiveness of our approach. Qualitative segmentation predictions further validate the high-quality outputs produced by our model. Our findings underscore the potential of transfer learning and ensemble approaches in medical image segmentation, indicating a substantial enhancement in clinical decision-making and patient care. Despite facing challenges related to post-processing and domain gaps, our study sets a new precedent for future research for brain tumor segmentation. The docker image for the code and models has been made publicly available, https://hub.docker.com/r/razeineldin/ht-cnns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
