Esophagus segmentation from planning CT images using an atlas-based deep learning approach

Journal article

Background and Objective: One of the main steps in the planning of radiotherapy (RT) is the segmentation of organs at risk (OARs) in Computed Tomography (CT). The esophagus is one of the most difficult OARs to segment. The boundaries between the esophagus and other surrounding tissues are not well-defined, and it is presented in several slices of the CT. Thus, manually segment the esophagus requires a lot of experience and takes time. This difficulty in manual segmentation combined with fatigue due to the number of slices to segment can cause human errors. To address these challenges, computational solutions for analyzing medical images and proposing automated segmentation have been developed and explored in recent years. In this work, we propose a fully automatic method for esophagus segmentation for better planning of radiotherapy in CT.

Methods: The proposed method is a fully automated segmentation of the esophagus, consisting of 5 main steps: (a) image acquisition; (b) VOI segmentation; (c) preprocessing; (d) esophagus segmentation; and (e) segmentation refinement.

Results: The method was applied in a database of 36 CT acquired from 3 different institutes. It achieved the best results in literature so far: Dice coefficient value of 82.15%, Jaccard Index of 70.21%, accuracy of 99.69%, sensitivity of 90.61%, specificity of 99.76%, and Hausdorff Distance of 6.1030 mm.

Conclusions: With the achieved results, we were able to show how promising the method is, and that applying it in large medical centers, where esophagus segmentation is still an arduous and challenging task, can be of great help to the specialists.

Authors: Bandeira Diniz, JO; Lima Ferreira, J; Bandeira Diniz, PH; Corrêa Silva, A; Cardoso de Paiva, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Computer Methods and Programs in Biomedicine
• Volume: 197
• Article number: 105685
• Publication date: 2020-08-07
• Acceptance date: 2020-07-28
• DOI: 10.1016/j.cmpb.2020.105685
• ISSN: 0169-2607
• Language: English
• Keywords: FFR; convolutional neural networks; radiotherapy; organs at risk; esophagus segmentation; computed tomography