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Automatic Pancreas Segmentation Based on Cascaded Network Considering Pancreatic Uncertainty in Abdominal CT Images
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복부 CT 영상에서 췌장의 불확실성을 고려한 계층적 네트워크 기반 자동 췌장 분할

논문 기본 정보

Type
Academic journal
Author
Hyeon Dham Yoon (서울여자대학교) Hyeonjin Kim (서울여자대학교) Helen Hong (서울여자대학교)
Journal
Korean Institute of Information Scientists and Engineers Journal of KIISE Vol.48 No.5 KCI Excellent Accredited Journal
Published
2021.5
Pages
548 - 555 (8page)
DOI
10.5626/JOK.2021.48.5.548

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Automatic Pancreas Segmentation Based on Cascaded Network Considering Pancreatic Uncertainty in Abdominal CT Images
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Pancreas segmentation from abdominal CT images is a prerequisite step for understanding the shape of the pancreas in pancreatic cancer detection. In this paper, we propose an automatic pancreas segmentation method based on a deep convolutional neural network(DCNN) that considers information about the uncertain regions generated by the positional and morphological diversity of the pancreas in abdominal CT images. First, intensity and spacing normalizations are performed in the whole abdominal CT images. Second, the pancreas is localized using 2.5D segmentation networks based on U-Net on the axial, coronal, and sagittal planes and by combining through a majority voting. Third, pancreas segmentation is performed in the localized volume using a 3D U-Net-based segmentation network that takes into account the information about the uncertain areas of the pancreas. The average DSC of pancreas segmentation was 83.50%, which was 10.30%p, 10.44%p, 6.52%p, 1.14%p, and 3.95%p higher than the segmentation method using 2D U-Net at axial view, coronal view, sagittal view, majority voting of the three planes, and 3D U-Net at localized volume, respectively.
#딥 컨볼루션 신경망(DCNN) #췌장 분할 #복부 CT 영상 #불확실성 #deep convolutional neural network (DCNN) #pancreas segmentation #abdominal CT images #U-Net #uncertainty

Contents

요약
Abstract
1. 서론
2. 복부 CT 영상에서 췌장 자동 분할
3. 실험 및 결과 분석
4. 결론
References

References (12)

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National Cancer Center. Cancer Statistics for 2017 [Internet]. Goyang: National Cancer Center; 2020. Available from: https://ncc.re.kr/cancerStatsView.ncc?bbsnum=518&searchKey=total&searchValue=&pageNum=1, 2020. M. Ducreux et al., "Cancer of the pancreas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up," Annals of Oncology, Vol. 26, supplement 5, pp. v56-v68, 2015. google schola Y. Man et al., "Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net," IEEE transactions on medical imaging, 2019. google schola B. Giddwani et al., "Deep Dilated V-Net for 3D Volume Segmentation of Pancreas in CT images," 7th International Conference on Signal Processing and Integrated Networks(SPIN), pp. 591-596, 2020. google schola Y. Zhou et al., "A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans," Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, pp. 693-701, 2017. google schola

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