Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm
One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus i...
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MDPI AG
2021-07-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4556 |
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| author | Minh-Trieu Tran Soo-Hyung Kim Hyung-Jeong Yang Guee-Sang Lee In-Jae Oh Sae-Ryung Kang |
| author_facet | Minh-Trieu Tran Soo-Hyung Kim Hyung-Jeong Yang Guee-Sang Lee In-Jae Oh Sae-Ryung Kang |
| author_sort | Minh-Trieu Tran |
| collection | DOAJ |
| description | One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images. To address these challenges, we propose a fully automated framework for the esophagus segmentation from CT images. The proposed method is based on the processing of slice images from the original three-dimensional (3D) image so that our method does not require large computational resources. We employ the spatial attention mechanism with the atrous spatial pyramid pooling module to locate the esophagus effectively, which enhances the segmentation performance. To optimize our model, we use group normalization because the computation is independent of batch sizes, and its performance is stable. We also used the simultaneous truth and performance level estimation (STAPLE) algorithm to reach robust results for segmentation. Firstly, our model was trained by k-fold cross-validation. And then, the candidate labels generated by each fold were combined by using the STAPLE algorithm. And as a result, Dice and Hausdorff Distance scores have an improvement when applying this algorithm to our segmentation results. Our method was evaluated on SegTHOR and StructSeg 2019 datasets, and the experiment shows that our method outperforms the state-of-the-art methods in esophagus segmentation. Our approach shows a promising result in esophagus segmentation, which is still challenging in medical analyses. |
| first_indexed | 2024-03-10T09:49:55Z |
| format | Article |
| id | doaj.art-824412cc51c14b2cb7d86d8bfa86b819 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:49:55Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-824412cc51c14b2cb7d86d8bfa86b8192023-11-22T02:51:05ZengMDPI AGSensors1424-82202021-07-012113455610.3390/s21134556Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE AlgorithmMinh-Trieu Tran0Soo-Hyung Kim1Hyung-Jeong Yang2Guee-Sang Lee3In-Jae Oh4Sae-Ryung Kang5Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Gwangju 500757, KoreaDepartment of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Gwangju 500757, KoreaDepartment of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Gwangju 500757, KoreaDepartment of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Gwangju 500757, KoreaDepartment of Internal Medicine, Chonnam National University Medical School and Hwasun Hospital, Hwasun 58128, KoreaDepartment of Nuclear Medicine, Chonnam National University Medical School and Hwasun Hospital, Hwasun 58128, KoreaOne essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images. To address these challenges, we propose a fully automated framework for the esophagus segmentation from CT images. The proposed method is based on the processing of slice images from the original three-dimensional (3D) image so that our method does not require large computational resources. We employ the spatial attention mechanism with the atrous spatial pyramid pooling module to locate the esophagus effectively, which enhances the segmentation performance. To optimize our model, we use group normalization because the computation is independent of batch sizes, and its performance is stable. We also used the simultaneous truth and performance level estimation (STAPLE) algorithm to reach robust results for segmentation. Firstly, our model was trained by k-fold cross-validation. And then, the candidate labels generated by each fold were combined by using the STAPLE algorithm. And as a result, Dice and Hausdorff Distance scores have an improvement when applying this algorithm to our segmentation results. Our method was evaluated on SegTHOR and StructSeg 2019 datasets, and the experiment shows that our method outperforms the state-of-the-art methods in esophagus segmentation. Our approach shows a promising result in esophagus segmentation, which is still challenging in medical analyses.https://www.mdpi.com/1424-8220/21/13/4556esophagus segmentationdeep learningspatial attention module |
| spellingShingle | Minh-Trieu Tran Soo-Hyung Kim Hyung-Jeong Yang Guee-Sang Lee In-Jae Oh Sae-Ryung Kang Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm Sensors esophagus segmentation deep learning spatial attention module |
| title | Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm |
| title_full | Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm |
| title_fullStr | Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm |
| title_full_unstemmed | Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm |
| title_short | Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm |
| title_sort | esophagus segmentation in ct images via spatial attention network and staple algorithm |
| topic | esophagus segmentation deep learning spatial attention module |
| url | https://www.mdpi.com/1424-8220/21/13/4556 |
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