Prostate Segmentation in MRI Images using Transfer Learning based Mask R-CNN

Maryam Shabbir; Zobia Suhail; Nida Hafeez; Najmus Saqib; Muhammad Farooq; Sghaier Guizani; Ateeq Ur Rehman; Habib Hamam

doi:10.2174/0115734056305021240603114137

ISSN: 1573-4056
E-ISSN: 1875-6603

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oa Prostate Segmentation in MRI Images using Transfer Learning based Mask R-CNN
Authors: Maryam Shabbir¹, Zobia Suhail², Nida Hafeez³, Najmus Saqib⁴, Muhammad Farooq⁵, Sghaier Guizani⁶, Ateeq Ur Rehman⁴ and Habib Hamam^8,9,10,11
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¹ Department of Computer Science, Bahria University, Lahore, Pakistan ² Department of Computer Science, University of the Punjab, Lahore, Pakistan, ³ School of Computer Science and Technology, University of Science and Technology of China, China ⁴ School of Computer Science and Technology, University of Science and Technology of China, China ⁵ Department of Computer Science, University of the Punjab, Lahore, Pakistan ⁶ College of Engineering, Alfaisal University, Riyadh, Saudi Arabia ⁷ School of Computing, Gachon University, Seongnam 13120, Republic of Korea ⁸ Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada ⁹ Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa ¹⁰ Hodmas University College, Taleh Area, Mogadishu, Somalia ¹¹ Bridges for Academic Excellence, Tunis, Centre Ville, Tunisia
Source: Current Medical Imaging, Volume 20, Issue 1, Jan 2024, e15734056305021
DOI: https://doi.org/10.2174/0115734056305021240603114137
- Received: 18 Feb 2024
- Accepted: 14 May 2024
- Available online: 01 Jan 2024

Abstract

Introduction

The second highest cause of death among males is Prostate Cancer (PCa) in America. Over the globe, it’s the usual case in men, and the annual PCa ratio is very surprising. Identical to other prognosis and diagnostic medical systems, deep learning-based automated recognition and detection systems (i.e., Computer Aided Detection (CAD) systems) have gained enormous attention in PCA.

Methods

These paradigms have attained promising results with a high segmentation, detection, and classification accuracy ratio. Numerous researchers claimed efficient results from deep learning-based approaches compared to other ordinary systems that utilized pathological samples.

Results

This research is intended to perform prostate segmentation using transfer learning-based Mask R-CNN, which is consequently helpful in prostate cancer detection.

Conclusion

Lastly, limitations in current work, research findings, and prospects have been discussed.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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References

DuttaA.K. MageswariR.U. GayathriA. Dallfin BruxellaJ.M. IshakM.K. MostafaS.M. HamamH. Barnacles mating optimizer with deep transfer learning enabled biomedical malaria parasite detection and classification.Comput. Intell. Neurosci.2022202211210.1155/2022/777631935694571
[Google Scholar]
haqI. A deep learning approach for the detection and counting of colon cancer cells (HT-29 cells) bunches and im-purities.PeerJ Comp. Sci.20239e165110.7717/peerj‑cs.1651
[Google Scholar]
MahmoodF. ArshadJ. Ben OthmanM.T. HayatM.F. BhattiN. JafferyM.H. RehmanA.U. HamamH. Implementation of an intelligent exam supervision system using deep learning algorithms.Sensors20222217638910.3390/s2217638936080848
[Google Scholar]
RajeswariD. Structural health monitoring of aircraft through prediction of delamination using machine learning.PeerJ Comp. Sci.202410exxx
[Google Scholar]
Muhammad HussainN. RehmanA.U. OthmanM.T.B. ZafarJ. ZafarH. HamamH. Accessing artificial intelligence for fetus health status using hybrid deep learning algorithm (AlexNet-SVM) on cardiotocographic data.Sensors20222214510310.3390/s2214510335890783
[Google Scholar]
ShafiqM. ChoiJ.G. CheikhrouhouO. HamamH. Advances in IoMT for healthcare systems.Sensors20232411010.3390/s2401001038202871
[Google Scholar]
BroschT. PetersJ. GrothA. StehleT. WeeseJ. Deep learning-based boundary detection for model-based segmentation with application to mr prostate segmentation.International Conference on Medical Image Computing and Computer-Assisted InterventionSpringer201851552210.1007/978‑3‑030‑00937‑3_59
[Google Scholar]
ComelliA. DahiyaN. StefanoA. VernuccioF. PortogheseM. CutaiaG. BrunoA. SalvaggioG. YezziA. Deep learning-based methods for prostate segmentation in magnetic resonance imaging.Appl. Sci.202111278210.3390/app1102078233680505
[Google Scholar]
Zavala-RomeroO. BretoA.L. XuI.R. ChangY.C.C. GautneyN. Dal PraA. AbramowitzM.C. PollackA. StoyanovaR. Segmentation of prostate and prostate zones using deep learning.Strahlenther. Onkol.20201961093294210.1007/s00066‑020‑01607‑x32221622
[Google Scholar]
CuocoloR. ComelliA. StefanoA. BenfanteV. DahiyaN. StanzioneA. CastaldoA. De LuciaD.R. YezziA. ImbriacoM. Deep learning whole-gland and zonal prostate segmentation on a public mri dataset.J. Magn. Reson. Imaging202154245245910.1002/jmri.2758533634932
[Google Scholar]
GillespieD. KendrickC. BoonI. BoonC. RattayT. YapM.H Deep learning in magnetic resonance prostate segmentation: A review and a new perspective.arXiv:2011.077952020
[Google Scholar]
TurkbeyB. HaiderM.A. Deep learning-based artificial intelligence applications in prostate MRI: brief summary.Br. J. Radiol.20229511312021056310.1259/bjr.2021056334860562
[Google Scholar]
CuocoloR. CipulloM.B. StanzioneA. UggaL. RomeoV. RadiceL. BrunettiA. ImbriacoM. Machine learning applications in prostate cancer magnetic resonance imaging.Eur. Radiol. Exp.2019313510.1186/s41747‑019‑0109‑231392526
[Google Scholar]
SahaA. HosseinzadehM. HuismanH End-to-end prostate cancer detection in bpmri via 3d cnns: Effect of attention mechanisms, clinical priori and decoupled false positive reduction.arXiv:2101.03244v102021
[Google Scholar]
ChenJ. WanZ. ZhangJ. LiW. ChenY. LiY. DuanY. Medical image segmentation and reconstruction of prostate tumor based on 3D AlexNet.Comput. Methods Programs Biomed.202120010587810.1016/j.cmpb.2020.10587833308904
[Google Scholar]
TianZ. LiuL. ZhangZ. FeiB. PSNet: prostate segmentation on MRI based on a convolutional neural network.J. Med. Imaging201852110.1117/1.JMI.5.2.02120829376105
[Google Scholar]
ChenW. ZhangY. HeJ. QiaoY. ChenY. ShiH. WuE.X. TangX. Prostate segmentation using 2d bridged u-net.2019 International Joint Conference on Neural Networks (IJCNN)IEEE201917
[Google Scholar]
NowlingR.J. BukowyJ. McGarryS.D. NenckaA.S. BlaskoO. UrbainJ. LowmanA. BarringtonA. BanerjeeA. IczkowskiK.A. Classification before segmentation: Improved u-net prostate segmentation.2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)IEEE20191410.1109/BHI.2019.8834494
[Google Scholar]
LiS. ChenY. YangS. LuoW. Cascade dense-unet for prostate segmentation in mr images.International Conference on Intelligent ComputingSpringer201948149010.1007/978‑3‑030‑26763‑6_46
[Google Scholar]
ZhangY. WuJ. ChenW. ChenY. TangX. Prostate segmentation using z-net.2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)IEEE2019111410.1109/ISBI.2019.8759554
[Google Scholar]
ZhuQ. DuB. YanP. Boundary-weighted domain adaptive neural network for prostate mr image segmentation.IEEE Trans. Med. Imaging202039375376310.1109/TMI.2019.293501831425022
[Google Scholar]
DaiZ. CarverE. LiuC. LeeJ. FeldmanA. ZongW. PantelicM. ElshaikhM. WenN Segmentation of the prostatic gland and the intraprostatic lesions on multiparametic mri using mask-rcnn.arXiv:1904.025752019
[Google Scholar]
BukowyJ.D. FossH. McGarryS.D. LowmanA. HurrellS. IckzkowskiK.A. BanerjeeA. BarringtonA. DaytonA. UnterinerJ. JacobsohnK. SeeW. NevalanianM. NenckaA. EthridgeT. JarrardD. LaVioletteP. Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network.FASEB J.201933S1lb12lb1210.1096/fasebj.2019.33.1_supplement.lb12
[Google Scholar]
ChahalE.S. PatelA. GuptaA. PurwarA. Unet based xception model for prostate cancer segmentation from mri images.Multimedia Tools Appl.20216117
[Google Scholar]
ShresthaS. AlsadoonA. PrasadP.W.C. SeherI. AlsadoonO.H. A novel solution of using deep learning for prostate cancer segmentation: Enhanced batch normalization.Multimedia Tools Appl.20218014212932131310.1007/s11042‑021‑10779‑2
[Google Scholar]
ZhuQ. DuB. TurkbeyB. ChoykeP.L. YanP. Deeply-supervised cnn for prostate segmentation, in: 2017 international joint conference on neural networks (IJCNN).IEEE201717818410.1109/IJCNN.2017.7965852
[Google Scholar]
KhanZ. YahyaN. AlsaihK. AliS.S.A. MeriaudeauF. Evaluation of deep neural networks for semantic segmentation of prostate in t2w mri.Sensors20202011318310.3390/s2011318332503330
[Google Scholar]
ArifM. SchootsI.G. Castillo TovarJ. BangmaC.H. KrestinG.P. RoobolM.J. NiessenW. VeenlandJ.F. Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI.Eur. Radiol.202030126582659210.1007/s00330‑020‑07008‑z32594208
[Google Scholar]

/content/journals/cmir/10.2174/0115734056305021240603114137

Prostate Segmentation in MRI Images using Transfer Learning based Mask R-CNN

Curr. Med. Imaging 20, e15734056305021 (2024); https://doi.org/10.2174/0115734056305021240603114137

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Article Type: Research Article

Keyword(s): CAD; Deep learning; Mask R-CNN; Prostate cancer; Prostate segmentation; Transfer learning

oa Prostate Segmentation in MRI Images using Transfer Learning based Mask R-CNN

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