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A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net
In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9394419/ https://www.ncbi.nlm.nih.gov/pubmed/35892756 http://dx.doi.org/10.3390/bioengineering9080343 |
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author | Altini, Nicola Brunetti, Antonio Napoletano, Valeria Pia Girardi, Francesca Allegretti, Emanuela Hussain, Sardar Mehboob Brunetti, Gioacchino Triggiani, Vito Bevilacqua, Vitoantonio Buongiorno, Domenico |
author_facet | Altini, Nicola Brunetti, Antonio Napoletano, Valeria Pia Girardi, Francesca Allegretti, Emanuela Hussain, Sardar Mehboob Brunetti, Gioacchino Triggiani, Vito Bevilacqua, Vitoantonio Buongiorno, Domenico |
author_sort | Altini, Nicola |
collection | PubMed |
description | In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface. |
format | Online Article Text |
id | pubmed-9394419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93944192022-08-23 A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net Altini, Nicola Brunetti, Antonio Napoletano, Valeria Pia Girardi, Francesca Allegretti, Emanuela Hussain, Sardar Mehboob Brunetti, Gioacchino Triggiani, Vito Bevilacqua, Vitoantonio Buongiorno, Domenico Bioengineering (Basel) Article In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface. MDPI 2022-07-26 /pmc/articles/PMC9394419/ /pubmed/35892756 http://dx.doi.org/10.3390/bioengineering9080343 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Altini, Nicola Brunetti, Antonio Napoletano, Valeria Pia Girardi, Francesca Allegretti, Emanuela Hussain, Sardar Mehboob Brunetti, Gioacchino Triggiani, Vito Bevilacqua, Vitoantonio Buongiorno, Domenico A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net |
title | A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net |
title_full | A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net |
title_fullStr | A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net |
title_full_unstemmed | A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net |
title_short | A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net |
title_sort | fusion biopsy framework for prostate cancer based on deformable superellipses and nnu-net |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9394419/ https://www.ncbi.nlm.nih.gov/pubmed/35892756 http://dx.doi.org/10.3390/bioengineering9080343 |
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