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Optimizing the Use of Radiologist Seed Points for Improved Multiple Sclerosis Lesion Segmentation

机译：优化放射科医师种子点用于改善多发性硬化病变分割

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Jon Mcausl; Roger C. Tam; Erick Wong; Andrew Riddehough; David K. B. Li;
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年度 2010
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1. Optimizing the Use of Radiologist Seed Points for Improved Multiple Sclerosis Lesion Segmentation [J] . McAusland J.Tam R. C.Wong E.Riddehough A.Li D. K. B. Biomedical Engineering, IEEE Transactions on . 2010,第11期

机译：优化使用放射科医师的种子点以改善多发性硬化症病变分割
2. Improved particle swarm optimized deep convolutional neural network with super-pixel clustering for multiple sclerosis lesion segmentation in brain MRI imaging [J] . Krishna Priya R., Chacko Susamma International journal for numerical methods in biomedical engineering . 2021,第9期

机译：改进的粒子群优化深卷积神经网络，具有超像素聚类，用于脑MRI成像中的多发性硬化病变分段
3. Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation [J] . M.M. Weeda, I. Brouwer, M.L. de Vos, NeuroImage: Clinical . 2019,第6期

机译：多发性硬化症中病变分割方法的比较：来自一个手动描绘的受试者的输入足以进行准确的病变分割
4. Improving Multiple Sclerosis Lesion Boundaries Segmentation by Convolutional Neural Networks with Focal Learning [C] . Gustavo Ulloa, Alejandro Veloz, Hector Allende-Cid, International Conference on Image Analysis and Recognition . 2020

机译：通过卷积神经网络与局灶学习改善多发性硬化症病变边界分割
5. Automatic Segmentation of Intramedullary Multiple Sclerosis Lesions [D] . Gros, Charley 2018

机译：髓内多发性硬化病灶的自动分割
6. Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation [O] . M.M. Weeda, I. Brouwer, M.L. de Vos, 2019

机译：多发性硬化症中病变分割方法的比较：来自一个手动描绘的受试者的输入足以进行准确的病变分割
7. Multiple Sclerosis lesion segmentation using an automatic multimodal Graph Cuts : Multiple Sclerosis lesion segmentation using an automatic multimodal Graph Cuts [O] . García-Lorenzo, Daniel, Lecoeur, Jérémy, Arnold, Douglas,, 2009

机译：使用自动多模式图切的多发性硬化病变分割：使用自动多模式图切的多发性硬化病变分割

Optimizing the Use of Radiologist Seed Points for Improved Multiple Sclerosis Lesion Segmentation

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