Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks.
Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks.
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DOI：
10.1109/tmi.2018.2806309
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发表时间：
2018-08
影响因子：
10.6
通讯作者：
Barratt DC
中科院分区：
文献类型：
--
作者：
Gibson E;Giganti F;Hu Y;Bonmati E;Bandula S;Gurusamy K;Davidson B;Pereira SP;Clarkson MJ;Barratt DC
在计算机断层扫描（CT）图像上自动分割腹部解剖可以支持诊断，治疗计划和治疗交付工作流程。使用统计模型和多图谱标签融合（MALF）的分割方法需要对腹部图像进行主体间配准，这对腹部图像来说是一个挑战，但没有配准的替代方法尚未达到对大多数腹部器官更高的精度。我们提出了一种基于深度学习的无配准分割算法，用于胰和胆道内镜手术中与导航相关的八个器官，包括胰腺、胃肠道（食管、胃、十二指肠）和周围器官（肝、脾、左肾、胆囊）。在一个包含90个受试者的多中心数据集上，我们直接将所提出方法的分割精度与现有的深度学习和MALF方法进行了交叉验证。所提出的方法在所有器官中获得了更高的Dice分数，而大多数器官的平均绝对距离较低，包括胰腺的Dice分数为0.78比0.71和0.74，胃的Dice分数为0.90比0.85、0.87和0.83，食道的Dice分数为0.76比0.68、0.69和0.66。我们得出结论，基于深度学习的分割代表了一种无配准的多器官腹部CT分割方法，其准确性可以超越现有方法，潜在地支持胃肠道内窥镜检查过程中的图像引导导航。
Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the GI tract (esophagus, stomach, duodenum) and surrounding organs (liver, spleen, left kidney, gallbladder). We directly compared the segmentation accuracy of the proposed method to existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 vs. 0.71, and 0.74 for the pancreas, 0.90 vs 0.85, 0.87 and 0.83 for the stomach and 0.76 vs 0.68, 0.69 and 0.66 for the esophagus. We conclude that deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures.