TY - JOUR
T1 - H-SegMed
T2 - A Hybrid Method for Prostate Segmentation in TRUS Images via Improved Closed Principal Curve and Improved Enhanced Machine Learning
AU - Peng, Tao
AU - Tang, Caiyin
AU - Wu, Yiyun
AU - Cai, Jing
N1 - Funding Information:
This work is partly supported by ITS/080/19.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/8
Y1 - 2022/8
N2 - Prostate segmentation is an important step in prostate volume estimation, multi-modal image registration, and patient-specific anatomical modeling for surgical planning and image-guided biopsy. Manual delineation of the prostate contour is time-consuming and prone to inter- and intra-observer variability. Accurate prostate segmentation in transrectal ultrasound images is particularly challenging due to the ambiguous boundary between the prostate and neighboring organs, the presence of shadow artifacts, heterogeneous intra-prostate image intensity, and inconsistent anatomical shapes. Therefore, in this study, we propose a novel hybrid segmentation method (H-SegMed) for accurate prostate segmentation in TRUS images. The method consists of two main steps: (1) an improved closed principal curve-based method was used to obtain the data sequence, in which only few radiologist-defined seed points were used as an approximate initialization; and (2) an enhanced machine learning method was used to achieve an accurate and smooth contour of the prostate. Our results show that the proposed model achieved superior segmentation performance compared with several other state-of-the-art models, achieving an average Dice similarity coefficient, Jaccard similarity coefficient (Ω), and accuracy of 96.5, 95.1, and 96.3%, respectively.
AB - Prostate segmentation is an important step in prostate volume estimation, multi-modal image registration, and patient-specific anatomical modeling for surgical planning and image-guided biopsy. Manual delineation of the prostate contour is time-consuming and prone to inter- and intra-observer variability. Accurate prostate segmentation in transrectal ultrasound images is particularly challenging due to the ambiguous boundary between the prostate and neighboring organs, the presence of shadow artifacts, heterogeneous intra-prostate image intensity, and inconsistent anatomical shapes. Therefore, in this study, we propose a novel hybrid segmentation method (H-SegMed) for accurate prostate segmentation in TRUS images. The method consists of two main steps: (1) an improved closed principal curve-based method was used to obtain the data sequence, in which only few radiologist-defined seed points were used as an approximate initialization; and (2) an enhanced machine learning method was used to achieve an accurate and smooth contour of the prostate. Our results show that the proposed model achieved superior segmentation performance compared with several other state-of-the-art models, achieving an average Dice similarity coefficient, Jaccard similarity coefficient (Ω), and accuracy of 96.5, 95.1, and 96.3%, respectively.
KW - Accurate prostate segmentation
KW - Greedy closed principal curve method
KW - Machine learning
KW - Memory-based adaptive differential evolution
KW - Principal curve
KW - Transrectal ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85130688530&partnerID=8YFLogxK
U2 - 10.1007/s11263-022-01619-3
DO - 10.1007/s11263-022-01619-3
M3 - Journal article
AN - SCOPUS:85130688530
SN - 0920-5691
VL - 130
SP - 1896
EP - 1919
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
IS - 8
ER -