Satushe Vaidehi, Vyas Vibha, Metkar Shilpa, Paul Singh Davinder
Department of Electronics & Telecommunication Engineering, COEP Technological University (COEP Tech), Pune, Maharashtra, India.
Department of Computer Science and Engineering, Pandit Deen Dayal Energy University, Gujarat, Gandhinagar, India.
Curr Med Imaging. 2025;21:e15734056344235. doi: 10.2174/0115734056344235241217155930.
The BraTS Generalizability Across Tumors (BraTS-GoAT) initiative addresses the critical need for robust and generalizable models in brain tumor segmentation. Despite advancements in automated segmentation techniques, the variability in tumor characteristics and imaging modalities across clinical settings presents a significant challenge.
This study aims to develop an advanced CNN-based model for brain tumor segmentation that enhances consistency and utility across diverse clinical environments. The objective is to improve the generalizability of CNN models by applying them to large-scale datasets and integrating robust preprocessing techniques.
The proposed approach involves the application of advanced CNN models to the BraTS 2024 challenge dataset, incorporating preprocessing techniques such as standardization, feature extraction, and segmentation. The model's performance was evaluated based on accuracy, mean Intersection over Union (IOU), average Dice coefficient, Hausdorff 95 score, precision, sensitivity, and specificity.
The model achieved an accuracy of 98.47%, a mean IOU of 0.8185, an average Dice coefficient of 0.7, an average Hausdorff 95 score of 1.66, a precision of 98.55%, a sensitivity of 98.40%, and a specificity of 99.52%. These results demonstrate a significant improvement over the current gold standard in brain tumor segmentation.
The findings of this study contribute to establishing benchmarks for generalizability in medical imaging, promoting the adoption of CNN-based brain tumor segmentation models in diverse clinical environments. This work has the potential to improve outcomes for patients with brain tumors by enhancing the reliability and effectiveness of automated segmentation techniques.
脑肿瘤分割通用模型（BraTS-GoAT）计划满足了对脑肿瘤分割中强大且通用模型的迫切需求。尽管自动分割技术取得了进展，但临床环境中肿瘤特征和成像方式的变异性带来了重大挑战。
本研究旨在开发一种基于卷积神经网络（CNN）的先进脑肿瘤分割模型，以提高在不同临床环境中的一致性和实用性。目标是通过将CNN模型应用于大规模数据集并整合强大的预处理技术来提高其通用性。
所提出的方法涉及将先进的CNN模型应用于BraTS 2024挑战数据集，纳入标准化、特征提取和分割等预处理技术。基于准确率、平均交并比（IOU）、平均骰子系数、豪斯多夫95分数、精确率、灵敏度和特异性对模型性能进行评估。
该模型的准确率达到98.47%，平均IOU为0.8185，平均骰子系数为0.7，平均豪斯多夫95分数为1.66，精确率为98.55%，灵敏度为98.40%，特异性为99.52%。这些结果表明相对于当前脑肿瘤分割的金标准有显著改进。
本研究结果有助于建立医学成像通用性的基准，促进基于CNN的脑肿瘤分割模型在不同临床环境中的应用。这项工作有可能通过提高自动分割技术的可靠性和有效性来改善脑肿瘤患者的治疗结果。
