Bianconi Francesco, Fravolini Mario Luca, Pizzoli Sofia, Palumbo Isabella, Minestrini Matteo, Rondini Maria, Nuvoli Susanna, Spanu Angela, Palumbo Barbara
Department of Engineering, Università degli Studi di Perugia, Perugia, Italy.
Department of Medicine and Surgery, Università degli Studi di Perugia, Perugia, Italy.
Quant Imaging Med Surg. 2021 Jul;11(7):3286-3305. doi: 10.21037/qims-20-1356.
Accurate segmentation of pulmonary nodules on computed tomography (CT) scans plays a crucial role in the evaluation and management of patients with suspicion of lung cancer (LC). When performed manually, not only the process requires highly skilled operators, but is also tiresome and time-consuming. To assist the physician in this task several automated and semi-automated methods have been proposed in the literature. In recent years, in particular, the appearance of deep learning has brought about major advances in the field.
Twenty-four (12 conventional and 12 based on deep learning) semi-automated-'one-click'-methods for segmenting pulmonary nodules on CT were evaluated in this study. The experiments were carried out on two datasets: a proprietary one (383 images from a cohort of 111 patients) and a public one (259 images from a cohort of 100). All the patients had a positive transcript for suspect pulmonary nodules.
The methods based on deep learning clearly outperformed the conventional ones. The best performance [Sørensen-Dice coefficient (DSC)] in the two datasets was, respectively, 0.853 and 0.763 for the deep learning methods, and 0.761 and 0.704 for the traditional ones.
Deep learning is a viable approach for semi-automated segmentation of pulmonary nodules on CT scans.
在计算机断层扫描（CT）图像上准确分割肺结节在疑似肺癌（LC）患者的评估和管理中起着至关重要的作用。手动进行分割时，不仅该过程需要高技能的操作人员，而且还很繁琐且耗时。为了协助医生完成这项任务，文献中提出了几种自动化和半自动化方法。近年来，尤其是深度学习的出现给该领域带来了重大进展。
本研究评估了24种（12种传统方法和12种基于深度学习的方法）用于在CT上分割肺结节的半自动“一键式”方法。实验在两个数据集上进行：一个专有数据集（来自111名患者队列的383张图像）和一个公共数据集（来自100名患者队列的259张图像）。所有患者的可疑肺结节转录本均为阳性。
基于深度学习的方法明显优于传统方法。在两个数据集中，深度学习方法的最佳性能[索伦森-戴斯系数（DSC）]分别为0.853和0.763，传统方法分别为0.761和0.704。
深度学习是CT扫描上肺结节半自动分割的一种可行方法。
