Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images
Joint Authors
Nai, Ying-Hwey
Teo, Bernice W.
Tan, Nadya L.
Chua, Koby Yi Wei
Wong, Chun Kit
O’Doherty, Sophie
Stephenson, Mary C.
Schaefferkoetter, Josh
Thian, Yee Liang
Chiong, Edmund
Reilhac, Anthonin
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-21
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Prostate segmentation in multiparametric magnetic resonance imaging (mpMRI) can help to support prostate cancer diagnosis and therapy treatment.
However, manual segmentation of the prostate is subjective and time-consuming.
Many deep learning monomodal networks have been developed for automatic whole prostate segmentation from T2-weighted MR images.
We aimed to investigate the added value of multimodal networks in segmenting the prostate into the peripheral zone (PZ) and central gland (CG).
We optimized and evaluated monomodal DenseVNet, multimodal ScaleNet, and monomodal and multimodal HighRes3DNet, which yielded dice score coefficients (DSC) of 0.875, 0.848, 0.858, and 0.890 in WG, respectively.
Multimodal HighRes3DNet and ScaleNet yielded higher DSC with statistical differences in PZ and CG only compared to monomodal DenseVNet, indicating that multimodal networks added value by generating better segmentation between PZ and CG regions but did not improve the WG segmentation.
No significant difference was observed in the apex and base of WG segmentation between monomodal and multimodal networks, indicating that the segmentations at the apex and base were more affected by the general network architecture.
The number of training data was also varied for DenseVNet and HighRes3DNet, from 20 to 120 in steps of 20.
DenseVNet was able to yield DSC of higher than 0.65 even for special cases, such as TURP or abnormal prostate, whereas HighRes3DNet’s performance fluctuated with no trend despite being the best network overall.
Multimodal networks did not add value in segmenting special cases but generally reduced variations in segmentation compared to the same matched monomodal network.
American Psychological Association (APA)
Nai, Ying-Hwey& Teo, Bernice W.& Tan, Nadya L.& Chua, Koby Yi Wei& Wong, Chun Kit& O’Doherty, Sophie…[et al.]. 2020. Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1139641
Modern Language Association (MLA)
Nai, Ying-Hwey…[et al.]. Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1139641
American Medical Association (AMA)
Nai, Ying-Hwey& Teo, Bernice W.& Tan, Nadya L.& Chua, Koby Yi Wei& Wong, Chun Kit& O’Doherty, Sophie…[et al.]. Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1139641
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139641