Kidney tumor segmentation in ultrasound images using adaptive sub-regional evolution level set models.
10.7507/1001-5515.201902011
- Author:
Xiaoliang XIONG
1
;
Yi GUO
2
,
3
;
Yuanyuan WANG
2
,
4
;
Dai ZHANG
5
,
6
,
7
,
8
;
Zhaoxiang YE
6
,
7
,
8
,
9
;
Sheng ZHANG
5
,
6
,
7
,
8
;
Xiaojie XIN
5
,
6
,
7
,
8
Author Information
1. Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China.
2. Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China
3. Key Laboratory of Medical Computer Assisted Intervention of Shanghai, Shanghai 200433, P.R.China.guoyi@fudan.edu.cn.
4. Key Laboratory of Medical Computer Assisted Intervention of Shanghai, Shanghai 200433, P.R.China.yywang@fudan.edu.cn.
5. Department of Ultrasound, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, P.R.China
6. National Clinical Research Center for Cancer, Tianjin 300060, P.R.China
7. KeyLaboraryof Cancer Prevention and Therapy, Tianjin 300060, P.R.China
8. Tianjin's Clinical Research Center for Cancer, Tianjin 300060, P.R.China.
9. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, P.R.China
- Publication Type:Journal Article
- Keywords:
adaptive sub-regional evolution;
centroid;
kidney tumor;
level set;
ultrasound image
- MeSH:
Algorithms;
Fetal Growth Retardation;
Humans;
Image Processing, Computer-Assisted;
Kidney Neoplasms;
Osteochondrodysplasias;
Ultrasonography
- From:
Journal of Biomedical Engineering
2019;36(6):945-956
- CountryChina
- Language:Chinese
-
Abstract:
Kidney tumor is one of the diseases threatening human health. Ultrasound is widely applied in kidney tumor diagnosis due to its high popularization, low price and no radiation. Accurate segmentation of kidney tumor is the basis of precise treatment. Kidney tumors often grow in the middle of cortex, so that segmentation is easy disturbed by nearby organs. Besides, ultrasound images own low contrast and large speckle, leading to difficult segmentation. This paper proposed a novel kidney tumor segmentation method in ultrasound images using adaptive sub-regional evolution level set models (ASLSM). Regions of interest are firstly divided into subareas. Secondly, object function is designed by integrating inside and outside energy and gradient, in which the ratio of these two parts are adjusted adaptively. Thirdly, ASLSM adapts convolution radius and curvature according to centroid principle and similarity inside and outside zero level set. Hausdorff distance (HD) of (8.75 ± 4.21) mm, mean absolute distance (MAD) of (3.26 ± 1.69) mm, dice-coefficient (DICE) of 0.93 ± 0.03 were obtained in the experiment. Compared with traditional ultrasound segmentation method, ASLSM is more accurate in kidney tumor segmentation. ASLSM may offer convenience for doctor to locate and diagnose kidney tumor in the future.
