Abstract
Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector. In this paper, we propose a novel boundary detection method by exploiting the power of regression forest for prostate and rectum segmentation. The contributions of this paper are as follows: (1) we introduce regression forest as a local boundary regressor to vote the entire boundary of a target organ, which avoids training a large number of landmark detectors and building an accurate 3D vertex correspondence for each landmark detector; (2) an auto-context model is integrated with regression forest to improve the accuracy of the boundary regression; (3) we further combine a deformable segmentation method with the proposed local boundary regressor for the final organ segmentation by integrating organ shape priors. Our method is evaluated on a planning CT image dataset with 70 images from 70 different patients. The experimental results show that our proposed boundary regression method outperforms the conventional boundary classification method in guiding the deformable model for prostate and rectum segmentations. Compared with other state-of-the-art methods, our method also shows a competitive performance.
| Original language | English |
|---|---|
| Pages (from-to) | 345-356 |
| Number of pages | 12 |
| Journal | Medical Image Analysis |
| Volume | 26 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 Dec 1 |
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Keywords
- Deformable segmentation
- Local boundary regression
- Prostate segmentation
- Rectum segmentation
- Regression forest
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Radiology Nuclear Medicine and imaging
- Health Informatics
- Radiological and Ultrasound Technology
Cite this
Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images. / Shao, Yeqin; Gao, Yaozong; Wang, Qian; Yang, Xin; Shen, Dinggang.
In: Medical Image Analysis, Vol. 26, No. 1, 01.12.2015, p. 345-356.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images
AU - Shao, Yeqin
AU - Gao, Yaozong
AU - Wang, Qian
AU - Yang, Xin
AU - Shen, Dinggang
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector. In this paper, we propose a novel boundary detection method by exploiting the power of regression forest for prostate and rectum segmentation. The contributions of this paper are as follows: (1) we introduce regression forest as a local boundary regressor to vote the entire boundary of a target organ, which avoids training a large number of landmark detectors and building an accurate 3D vertex correspondence for each landmark detector; (2) an auto-context model is integrated with regression forest to improve the accuracy of the boundary regression; (3) we further combine a deformable segmentation method with the proposed local boundary regressor for the final organ segmentation by integrating organ shape priors. Our method is evaluated on a planning CT image dataset with 70 images from 70 different patients. The experimental results show that our proposed boundary regression method outperforms the conventional boundary classification method in guiding the deformable model for prostate and rectum segmentations. Compared with other state-of-the-art methods, our method also shows a competitive performance.
AB - Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector. In this paper, we propose a novel boundary detection method by exploiting the power of regression forest for prostate and rectum segmentation. The contributions of this paper are as follows: (1) we introduce regression forest as a local boundary regressor to vote the entire boundary of a target organ, which avoids training a large number of landmark detectors and building an accurate 3D vertex correspondence for each landmark detector; (2) an auto-context model is integrated with regression forest to improve the accuracy of the boundary regression; (3) we further combine a deformable segmentation method with the proposed local boundary regressor for the final organ segmentation by integrating organ shape priors. Our method is evaluated on a planning CT image dataset with 70 images from 70 different patients. The experimental results show that our proposed boundary regression method outperforms the conventional boundary classification method in guiding the deformable model for prostate and rectum segmentations. Compared with other state-of-the-art methods, our method also shows a competitive performance.
KW - Deformable segmentation
KW - Local boundary regression
KW - Prostate segmentation
KW - Rectum segmentation
KW - Regression forest
UR - http://www.scopus.com/inward/record.url?scp=84949104206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949104206&partnerID=8YFLogxK
U2 - 10.1016/j.media.2015.06.007
DO - 10.1016/j.media.2015.06.007
M3 - Article
C2 - 26439938
AN - SCOPUS:84949104206
VL - 26
SP - 345
EP - 356
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
IS - 1
ER -