The Medical Segmentation Decathlon.
The Medical Segmentation Decathlon.
复制标题
DOI：
10.1038/s41467-022-30695-9
复制
发表时间：
2022-07-15
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
--
作者：
国际挑战已经成为图像分析算法比较评估的事实上的标准。虽然分割是研究最广泛的医学图像处理任务，但各种挑战已经组织起来，只关注特定的临床任务。我们组织了医学分割十项全能（MSD）——一项生物医学图像分析挑战，其中算法在众多任务和模式中竞争，以研究能够在多个任务中表现良好的方法能够很好地推广到以前未见过的任务，并可能优于定制设计的解决方案的假设。MSD的结果证实了这一假设，而且，MSD赢家在接下来的两年里继续很好地推广到其他广泛的临床问题。本研究得出三个主要结论：(1)最先进的图像分割算法在未见任务上进行再训练时具有良好的泛化能力；(2)跨多个任务的一致性算法性能是算法可泛化性的有力替代指标；(3)准确的人工智能分割模型的训练现在商品化给了不精通人工智能模型训练的科学家。国际挑战已经成为图像分析算法比较评估的事实上的标准。在这里，作者展示了生物医学图像分割挑战的结果，表明能够在多个任务上表现良好的方法将很好地推广到以前未见过的任务。
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training. International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Here, the authors present the results of a biomedical image segmentation challenge, showing that a method capable of performing well on multiple tasks will generalize well to a previously unseen task.
登录
查看更多内容
影响因子：
10.9
作者：
Maier-Hein, Lena;Reinke, Annika;Landman, Bennett A.
通讯作者：
Landman, Bennett A.
影响因子：
8.8
作者：
He, Xin;Zhao, Kaiyong;Chu, Xiaowen
通讯作者：
Chu, Xiaowen
影响因子：
7.4
作者：
Nikolov S;Blackwell S;Zverovitch A;Mendes R;Livne M;De Fauw J;Patel Y;Meyer C;Askham H;Romera-Paredes B;Kelly C;Karthikesalingam A;Chu C;Carnell D;Boon C;D'Souza D;Moinuddin SA;Garie B;McQuinlan Y;Ireland S;Hampton K;Fuller K;Montgomery H;Rees G;Suleyman M;Back T;Hughes CO;Ledsam JR;Ronneberger O
通讯作者：
Ronneberger O
影响因子：
16.6
作者：
Maier-Hein, Lena;Eisenmann, Matthias;Kopp-Schneider, Annette
通讯作者：
Kopp-Schneider, Annette
基于深度学习的鼻咽癌计算机断层扫描图像中危险器官的检测和分割，用于放射治疗计划
影响因子：
5.9
作者：
Liang, Shujun;Tang, Fan;Zhang, Yu
通讯作者：
Zhang, Yu