Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Altini, Nicola, Brunetti, Antonio, Napoletano, Valeria Pia, Girardi, Francesca, Allegretti, Emanuela, Hussain, Sardar Mehboob, Brunetti, Gioacchino, Triggiani, Vito, Bevilacqua, Vitoantonio, Buongiorno, Domenico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784771486011097088
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
work_keys_str_mv AT altininicola afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT brunettiantonio afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT napoletanovaleriapia afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT girardifrancesca afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT allegrettiemanuela afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT hussainsardarmehboob afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT brunettigioacchino afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT triggianivito afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT bevilacquavitoantonio afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT buongiornodomenico afusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT altininicola fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT brunettiantonio fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT napoletanovaleriapia fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT girardifrancesca fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT allegrettiemanuela fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT hussainsardarmehboob fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT brunettigioacchino fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT triggianivito fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT bevilacquavitoantonio fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet
AT buongiornodomenico fusionbiopsyframeworkforprostatecancerbasedondeformablesuperellipsesandnnunet