Automatic segmentation of anatomical structures is critical for many medical applications. However, the results are not always clinically acceptable and require tedious manual revision. Here, we present a novel concept called artificial intelligence assisted contour revision (AIACR) and demonstrate its feasibility. The proposed clinical workflow of AIACR is as follows given an initial contour that requires a clinicians revision, the clinician indicates where a large revision is needed, and a trained deep learning (DL) model takes this input to update the contour. This process repeats until a clinically acceptable contour is achieved. The DL model is designed to minimize the clinicians input at each iteration and to minimize the number of iterations needed to reach acceptance. In this proof-of-concept study, we demonstrated the concept on 2D axial images of three head-and-neck cancer datasets, with the clinicians input at each iteration being one mouse click on the desired location of the contour segment. The performance of the model is quantified with Dice Similarity Coefficient (DSC) and 95th percentile of Hausdorff Distance (HD95). The average DSC/HD95 (mm) of the auto-generated initial contours were 0.82/4.3, 0.73/5.6 and 0.67/11.4 for three datasets, which were improved to 0.91/2.1, 0.86/2.4 and 0.86/4.7 with three mouse clicks, respectively. Each DL-based contour update requires around 20 ms. We proposed a novel AIACR concept that uses DL models to assist clinicians in revising contours in an efficient and effective way, and we demonstrated its feasibility by using 2D axial CT images from three head-and-neck cancer datasets.
翻译:解剖结构的自动分解对于许多医疗应用来说至关重要。 但是, 其结果并不总是在临床上可以接受, 并且需要经过训练的深层次学习模式对更新轮廓至关重要。 这一过程在临床上并不总是可以接受, 并且需要经过精心修改。 在这里, 我们展示了一个名为人工智能辅助等深层修改( AIACR) 的新概念, 并展示了它的可行性。 在这项测试研究中, 我们展示了3个头部和颈部癌症数据集的2D轴轴图像的概念, 临床医生在每升一级需要做大量修改, 受过训练的深层学习模型( DL) 将这种输入用于更新轮廓。 这个过程将重复到临床可接受的等深层。 DL模型的功能被量化, 将每升2升为每升0. 8 4 的临床医生输入值输入2D.