医学影像中的高级肿瘤分割：针对BraTS 2023成人胶质瘤和儿科肿瘤任务的集成方法
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks
AI总结：
本研究提出一种集成方法，结合SegResNet和MedNeXt模型及后处理，用于BraTS 2023成人胶质瘤和儿科肿瘤分割，在成人胶质瘤任务中获第三名，Dice和HD95分别为0.8313和36.38。
摘要：
自动分割被证明是在医学图像中精确检测肿瘤的有价值工具。肿瘤类型的准确识别和分割对于诊断、监测和治疗高度致命的脑肿瘤至关重要。BraTS挑战赛为研究人员提供了一个平台，通过参与专注于肿瘤分割的开放挑战来解决这一问题。本研究概述了我们在BraTS 2023挑战中两个不同任务（成人胶质瘤和儿科肿瘤）背景下分割肿瘤的方法。我们的方法利用两个基于编码器-解码器的CNN模型，即SegResNet和MedNeXt，来分割肿瘤的三个不同子区域。我们进一步引入了一套稳健的后处理来改善分割，特别是针对新引入的BraTS 2023指标。我们的方法细节和全面的性能分析在本工作中进行了阐述。我们提出的方法在BraTS 2023成人胶质瘤分割挑战中获得第三名，在测试集上平均Dice和HD95分数分别为0.8313和36.38。
英文摘要：
Automated segmentation proves to be a valuable tool in precisely detecting tumors within medical images. The accurate identification and segmentation of tumor types hold paramount importance in diagnosing, monitoring, and treating highly fatal brain tumors. The BraTS challenge serves as a platform for researchers to tackle this issue by participating in open challenges focused on tumor segmentation. This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors. Our approach leverages two encoder-decoder-based CNN models, namely SegResNet and MedNeXt, for segmenting three distinct subregions of tumors. We further introduce a set of robust postprocessing to improve the segmentation, especially for the newly introduced BraTS 2023 metrics. The specifics of our approach and comprehensive performance analyses are expounded upon in this work. Our proposed approach achieves third place in the BraTS 2023 Adult Glioma Segmentation Challenges with an average of 0.8313 and 36.38 Dice and HD95 scores on the test set, respectively.
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