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Daniel Capellán-Martín^1,2, Zhifan Jiang¹, Abhijeet Parida¹

¹Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC 20010, USA.

Brain tumor segmentation, and cross-modality domain adaptation for medical image segmentation : MICCAI challenges, BraTS 2023 and CrossMoDA 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12 and 8, 2024 : proc...

|December 24, 2025

概括

这项研究引入了一种深度学习组合,用于从MRI扫描中对儿科脑瘤,脑膜瘤和脑转移进行细分. 该方法在BraTS挑战中获得了顶级排名,提高了瘤细分的准确性,以改善患者护理.

科学领域:

医疗成像医学成像
人工智能的人工智能
神经瘤学神经瘤学

背景情况:

在多参数MRI中精确的脑瘤细分对于定量分析,临床试验和个性化患者护理至关重要.
这种分析有助于诊断和预后的临床决策.
2023年大脑瘤细分 (BraTS) 挑战扩展到8个任务,有4,500例.

研究的目的:

开发和评估一个深度学习整体策略,用于在BraTS挑战中对新加入的瘤类型进行细分.
评估儿童脑瘤 (PED),脑内膜瘤 (MEN) 和大脑转移 (MET) 的表现.

主要方法:

一个整体战略,将最先进的 nnU-Net 和 Swin UNETR 模型结合起来,以区域为基础.
实施一个有针对性的后处理策略,使用交叉验证值搜索来改善瘤子区域细分.
对PED, MEN和MET任务的未见测试案例进行评估.

主要成果:

获得的病变智能PED的子得分:0.653 (增强瘤),0.809 (瘤核心),0.826 (整个瘤).
获得的病变智能子分数男性:0.876 (增强瘤),0.867 (瘤核心),0.849 (整个瘤).
获得的病变智能子得分MET:0.555 (增强瘤),0.6 (瘤核心),0.58 (整个瘤).
在BraTS挑战中排名第一的PED,第三的MEN,第四的MET.

结论:

拟议的深度学习整体策略有效地细分儿科脑瘤,脑膜瘤和脑转移.
该方法在具有挑战性的BraTS 2023任务中表现出竞争力,排名很高.
这种方法有可能推进定量分析,并支持神经瘤学中的临床决策.

关键词:

大脑瘤的细分脑瘤的细分深度学习是一种深度学习.这就是为什么MRI是MRI.阴道瘤是发生在阴道上的人.发生转移的转移.儿科大脑瘤儿科大脑瘤

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