ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation.
ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation.
复制标题
DOI：
10.1016/j.nicl.2021.102854
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Marcille M
中科院分区：
文献类型：
--
作者：
Zhang H;Zhang J;Li C;Sweeney EM;Spincemaille P;Nguyen TD;Gauthier SA;Wang Y;Marcille M
引入了一种新算法 ALL-Net 来改进 MS 病变分割。 ALL-Net 将解剖坐标信息集成到神经网络中。 ALL-Net 集成了 lesion-wise 损失函数来改进小病变检测。 ALL-Net 对于小型和大型 MS 病变数据集都具有鲁棒性。磁共振图像上多发性硬化症（MS）脑部病变的准确检测和分割对于疾病诊断和治疗具有重要意义。这是一项具有挑战性的任务，因为病变的大小、形状、位置和图像对比度差异很大。我们研究的目的是开发一种基于深度卷积神经网络的算法，该算法与解剖信息和病变损失函数 (ALL-Net) 相结合，用于快速、准确地自动分割 MS 病变。距离变换映射用于构建编码特定病变解剖信息的卷积模块。为了克服网络训练期间病变大小不平衡的问题并提高对小病变的检测，开发了一种病变损失函数，其中单个病变被建模为大小相等的球体。在 ISBI-2015 纵向 MS 病变分割挑战数据集（总共 19 个受试者）上，ALL-Net 的总体得分为 93.32，是表现最好的方法之一。在较大的康奈尔 MS 数据集（总共 176 个受试者）上，ALL-Net 显着改善了体素方面的指标（Dice 改善了 3.9% 到 35.3%，p 值范围为 p < 0.01 到 p < 0.0001，体素方面的精确回忆曲线的 AUC 改善了 2.1% 到 29.8%）和病变方面的指标（病变方面的 F1 分数）与领先的公开 MS 病灶分割工具相比，所有 p 值 p < 0.0001 提高了 12.6% 至 29.8%，病灶 ROC 曲线的 AUC 提高了 1.4% 至 20.0%。
A new algorithm, ALL-Net, is introduced to improve MS lesion segmentation. ALL-Net integrates anatomical coordinate information into the neural network. ALL-Net integrates lesion-wise loss function to improve small lesion detection. ALL-Net is robust to both small- and large-scale MS lesion datasets. Accurate detection and segmentation of multiple sclerosis (MS) brain lesions on magnetic resonance images are important for disease diagnosis and treatment. This is a challenging task as lesions vary greatly in size, shape, location, and image contrast. The objective of our study was to develop an algorithm based on deep convolutional neural network integrated with anatomic information and lesion-wise loss function (ALL-Net) for fast and accurate automated segmentation of MS lesions. Distance transformation mapping was used to construct a convolutional module that encoded lesion-specific anatomical information. To overcome the lesion size imbalance during network training and improve the detection of small lesions, a lesion-wise loss function was developed in which individual lesions were modeled as spheres of equal size. On the ISBI-2015 longitudinal MS lesion segmentation challenge dataset (19 subjects in total), ALL-Net achieved an overall score of 93.32 and was amongst the top performing methods. On the larger Cornell MS dataset (176 subjects in total), ALL-Net significantly improved both voxel-wise metrics (Dice improvement of 3.9% to 35.3% with p-values ranging from p < 0.01 to p < 0.0001, and AUC of voxel-wise precision-recall curve improvement of 2.1% to 29.8%) and lesion-wise metrics (lesion-wise F1 score improvement of 12.6% to 29.8% with all p-values p < 0.0001, and AUC of lesion-wise ROC curve improvement of 1.4% to 20.0%) compared to leading publicly available MS lesion segmentation tools.
登录
查看更多内容
影响因子：
5.7
作者：
Carass A;Roy S;Jog A;Cuzzocreo JL;Magrath E;Gherman A;Button J;Nguyen J;Prados F;Sudre CH;Jorge Cardoso M;Cawley N;Ciccarelli O;Wheeler-Kingshott CAM;Ourselin S;Catanese L;Deshpande H;Maurel P;Commowick O;Barillot C;Tomas-Fernandez X;Warfield SK;Vaidya S;Chunduru A;Muthuganapathy R;Krishnamurthi G;Jesson A;Arbel T;Maier O;Handels H;Iheme LO;Unay D;Jain S;Sima DM;Smeets D;Ghafoorian M;Platel B;Birenbaum A;Greenspan H;Bazin PL;Calabresi PA;Crainiceanu CM;Ellingsen LM;Reich DS;Prince JL;Pham DL
通讯作者：
Pham DL
影响因子：
10.6
作者：
Brosch, Tom;Tang, Lisa Y. W.;Tam, Roger
通讯作者：
Tam, Roger
DOI：
10.1109/tpami.2018.2858826
发表时间：
2020-02-01
影响因子：
23.6
作者：
Lin, Tsung-Yi;Goyal, Priya;Dollar, Piotr
通讯作者：
Dollar, Piotr
DOI：
10.1016/0146-664x(80)90054-4
发表时间：
1980-01-01
期刊：
COMPUTER GRAPHICS AND IMAGE PROCESSING
影响因子：
--
作者：
DANIELSSON, PE
通讯作者：
DANIELSSON, PE
影响因子：
19.7
作者：
Codella, Noel C. F.;Weinsaft, Jonathan W.;Wang, Yi
通讯作者：
Wang, Yi