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J. Imaging
Volume 8
Issue 5
10.3390/jimaging8050133
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Article

Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images

by
Massimo Salvi
1,
Bruno De Santi
2,
Bianca Pop
1,
Martino Bosco
3,
Valentina Giannini
4,5,
Daniele Regge
4,5,
Filippo Molinari
1 and
Kristen M. Meiburger
1,*
1
Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
2
Multi-Modality Medical Imaging (M3I), Technical Medical Centre, University of Twente, PB217, 7500 AE Enschede, The Netherlands
3
Department of Pathology, Ospedale Michele e Pietro Ferrero, 12060 Verduno, Italy
4
Department of Surgical Sciences, University of Turin, 10126 Turin, Italy
5
Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Caroline Petitjean
J. Imaging 2022, 8(5), 133; https://doi.org/10.3390/jimaging8050133
Received: 30 March 2022 / Revised: 6 May 2022 / Accepted: 9 May 2022 / Published: 11 May 2022
(This article belongs to the Special Issue Current Methods in Medical Image Segmentation)
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Abstract

Magnetic resonance imaging (MRI) has a growing role in the clinical workup of prostate cancer. However, manual three-dimensional (3D) segmentation of the prostate is a laborious and time-consuming task. In this scenario, the use of automated algorithms for prostate segmentation allows us to bypass the huge workload of physicians. In this work, we propose a fully automated hybrid approach for prostate gland segmentation in MR images using an initial segmentation of prostate volumes using a custom-made 3D deep network (VNet-T2), followed by refinement using an Active Shape Model (ASM). While the deep network focuses on three-dimensional spatial coherence of the shape, the ASM relies on local image information and this joint effort allows for improved segmentation of the organ contours. Our method is developed and tested on a dataset composed of T2-weighted (T2w) MRI prostatic volumes of 60 male patients. In the test set, the proposed method shows excellent segmentation performance, achieving a mean dice score and Hausdorff distance of 0.851 and 7.55 mm, respectively. In the future, this algorithm could serve as an enabling technology for the development of computer-aided systems for prostate cancer characterization in MR imaging. View Full-Text
Keywords: active shape models; automatic prostate segmentation; convolutional neural network; deep learning; hybrid framework; medical image segmentation; MRI active shape models; automatic prostate segmentation; convolutional neural network; deep learning; hybrid framework; medical image segmentation; MRI
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MDPI and ACS Style

Salvi, M.; De Santi, B.; Pop, B.; Bosco, M.; Giannini, V.; Regge, D.; Molinari, F.; Meiburger, K.M. Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images. J. Imaging 2022, 8, 133. https://doi.org/10.3390/jimaging8050133

AMA Style

Salvi M, De Santi B, Pop B, Bosco M, Giannini V, Regge D, Molinari F, Meiburger KM. Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images. Journal of Imaging. 2022; 8(5):133. https://doi.org/10.3390/jimaging8050133

Chicago/Turabian Style

Salvi, Massimo, Bruno De Santi, Bianca Pop, Martino Bosco, Valentina Giannini, Daniele Regge, Filippo Molinari, and Kristen M. Meiburger. 2022. "Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images" Journal of Imaging 8, no. 5: 133. https://doi.org/10.3390/jimaging8050133

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