» Articles » PMID: 31236743

Comprehensive Review of 3D Segmentation Software Tools for MRI Usable for Pelvic Surgery Planning

Overview
Journal J Digit Imaging
Publisher Springer
Date 2019 Jun 26
PMID 31236743
Citations 21
Authors
Affiliations
Soon will be listed here.
Abstract

Patient-specific 3D modeling is the first step towards image-guided surgery, the actual revolution in surgical care. Pediatric and adolescent patients with rare tumors and malformations should highly benefit from these latest technological innovations, allowing personalized tailored surgery. This study focused on the pelvic region, located at the crossroads of the urinary, digestive, and genital channels with important vascular and nervous structures. The aim of this study was to evaluate the performances of different software tools to obtain patient-specific 3D models, through segmentation of magnetic resonance images (MRI), the reference for pediatric pelvis examination. Twelve software tools freely available on the Internet and two commercial software tools were evaluated using T2-w MRI and diffusion-weighted MRI images. The software tools were rated according to eight criteria, evaluated by three different users: automatization degree, segmentation time, usability, 3D visualization, presence of image registration tools, tractography tools, supported OS, and potential extension (i.e., plugins). A ranking of software tools for 3D modeling of MRI medical images, according to the set of predefined criteria, was given. This ranking allowed us to elaborate guidelines for the choice of software tools for pelvic surgical planning in pediatric patients. The best-ranked software tools were Myrian Studio, ITK-SNAP, and 3D Slicer, the latter being especially appropriate if nerve fibers should be included in the 3D patient model. To conclude, this study proposed a comprehensive review of software tools for 3D modeling of the pelvis according to a set of eight criteria and delivered specific conclusions for pediatric and adolescent patients that can be directly applied to clinical practice.

Citing Articles

Considerations on Image Preprocessing Techniques Required by Deep Learning Models. The Case of the Knee MRIs.

Botnari A, Kadar M, Patrascu J Maedica (Bucur). 2024; 19(3):526-535.

PMID: 39553362 PMC: 11565144. DOI: 10.26574/maedica.2024.19.3.526.


Use of 3D foot and ankle puzzle enhances student understanding of the skeletal anatomy in the early years of medical school.

Al-Ani S, Chandla D, Delieu J, Yu S, Fratini A, Gkountiou R Surg Radiol Anat. 2024; 46(9):1429-1438.

PMID: 39060740 PMC: 11322274. DOI: 10.1007/s00276-024-03439-1.


Enhancing surgical planning for abdominal tumors in children through advanced 3D visualization techniques: a systematic review of future prospects.

Lopez P, Belgacem A, Sarnacki S, Arnaud A, Houari J, Piguet C Front Pediatr. 2024; 12:1386280.

PMID: 38863523 PMC: 11166126. DOI: 10.3389/fped.2024.1386280.


POP-Q Versus Upright MRI Distance Measurements: A Prospective Study in Patients with POP.

van der Steen A, Jochem K, Consten E, Simonis F, Grob A Int Urogynecol J. 2024; 35(6):1255-1261.

PMID: 38743071 PMC: 11245432. DOI: 10.1007/s00192-024-05802-7.


Automated 2D and 3D finite element overclosure adjustment and mesh morphing using generalized regression neural networks.

Andreassen T, Hume D, Hamilton L, Higinbotham S, Shelburne K Med Eng Phys. 2024; 126:104136.

PMID: 38621835 PMC: 11064159. DOI: 10.1016/j.medengphy.2024.104136.


References
1.
Haak D, Page C, Deserno T . A Survey of DICOM Viewer Software to Integrate Clinical Research and Medical Imaging. J Digit Imaging. 2015; 29(2):206-15. PMC: 4788610. DOI: 10.1007/s10278-015-9833-1. View

2.
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J, Pujol S . 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012; 30(9):1323-41. PMC: 3466397. DOI: 10.1016/j.mri.2012.05.001. View

3.
Lo Presti G, Carbone M, Ciriaci D, Aramini D, Ferrari M, Ferrari V . Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room. J Digit Imaging. 2015; 28(5):518-27. PMC: 4570900. DOI: 10.1007/s10278-015-9786-4. View

4.
Ferrari V, Carbone M, Cappelli C, Boni L, Melfi F, Ferrari M . Value of multidetector computed tomography image segmentation for preoperative planning in general surgery. Surg Endosc. 2011; 26(3):616-26. PMC: 3271225. DOI: 10.1007/s00464-011-1920-x. View

5.
Jenkinson M, Beckmann C, Behrens T, Woolrich M, Smith S . FSL. Neuroimage. 2011; 62(2):782-90. DOI: 10.1016/j.neuroimage.2011.09.015. View