Feasibility of Evaluating Result of Auto-segmentation of Target Volumes in Radiotherapy with Medical Consideration Index.
10.3969/j.issn.1671-7104.2021.05.022
- Author:
Yisong HE
1
;
Hang YU
1
;
Shengyuan ZHANG
2
;
Yong LUO
1
;
Yuchuan FU
1
Author Information
1. Department of Radiation Oncology, West China Hospital of Sichuan University, Chengdu, 610041.
2. Key Laboratory of Radiation Physics and Technology of Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, 610064.
- Publication Type:Journal Article
- Keywords:
Dice similarity coefficient;
automatic segmentation;
medical similarity index;
radiotherapy;
similarity of segmentation
- MeSH:
Feasibility Studies;
Radiotherapy Planning, Computer-Assisted
- From:
Chinese Journal of Medical Instrumentation
2021;45(5):573-579
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To explore the feasibility of using the bidirectional local distance based medical similarity index (MSI) to evaluate automatic segmentation on medical images.
METHODS:Taking the intermediate risk clinical target volume for nasopharyngeal carcinoma manually segmented by an experience radiation oncologist as region of interest, using Atlas-based and deep-learning-based methods to obtain automatic segmentation respectively, and calculated multiple MSI and Dice similarity coefficient (DSC) between manual segmentation and automatic segmentation. Then the difference between MSI and DSC was comparatively analyzed.
RESULTS:DSC values for Atlas-based and deep-learning-based automatic segmentation were 0.73 and 0.84 respectively. MSI values for them varied between 0.29~0.78 and 0.44~0.91 under different inside-outside-level.
CONCLUSIONS:It is feasible to use MSI to evaluate the results of automatic segmentation. By setting the penalty coefficient, it can reflect phenomena such as under-delineation and over-delineation, and improve the sensitivity of medical image contour similarity evaluation.
