Next Article in Journal
LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces
Previous Article in Journal
Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?
Previous Article in Special Issue
Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art
Article

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

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)
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
Show Figures

Figure 1

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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop