KneeBones3Dify: Open-source software for segmentation and 3D reconstruction of knee bones from MRI data

KneeBones3Dify is a Python software tool that supports detailed analysis of knee pathologies and preoperative planning for knee replacement surgery based on patient-specific 3D models. It produces printable 3D bones in a stereolithography file format by automatically segmenting the femur, patella, and tibia from high-resolution Magnetic Resonance (MR) images with nearly isotropic voxel dimensions. Our software avoids time-consuming and subjective manual segmentation by specialists, offering an accurate and efficient alternative employing GPU acceleration. We validated the results by computing objective metrics against the ground truth voxel-wise segmentation produced for a 3D MR image by specialists, who also confirmed the reconstruction accuracy qualitatively. KneeBones3Dify and annotated data are publicly available, enabling broader research and clinical practice use.

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Publication Details

Journal
SoftwareX
Published
2024-08-23
DOI
https://doi.org/10.1016/j.softx.2024.101854
Citations
4
Primary Topic
Total Knee Arthroplasty Outcomes
Type
article
Field-Weighted Citation Impact
2.29

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article

KneeBones3Dify: Open-source software for segmentation and 3D reconstruction of knee bones from MRI data

Gianluca De Lucia, Flavio Fazioli, Gabriele Pontillo, Ernesto Soscia et al.
4 citations
SoftwareX
Total Knee Arthroplasty Outcomes
2.29
article

KneeBones3Dify: Open-source software for segmentation and 3D reconstruction of knee bones from MRI data

Gianluca De Lucia, Flavio Fazioli, Gabriele Pontillo, Ernesto Soscia, Carla Langella, Lucia Maddalena, Laura Antonelli, Francesco Gregoretti, Diego Romano, Rosario Varriale, Carla Giusti
article en
4 citations

Abstract

KneeBones3Dify is a Python software tool that supports detailed analysis of knee pathologies and preoperative planning for knee replacement surgery based on patient-specific 3D models. It produces printable 3D bones in a stereolithography file format by automatically segmenting the femur, patella, and tibia from high-resolution Magnetic Resonance (MR) images with nearly isotropic voxel dimensions. Our software avoids time-consuming and subjective manual segmentation by specialists, offering an accurate and efficient alternative employing GPU acceleration. We validated the results by computing objective metrics against the ground truth voxel-wise segmentation produced for a 3D MR image by specialists, who also confirmed the reconstruction accuracy qualitatively. KneeBones3Dify and annotated data are publicly available, enabling broader research and clinical practice use.

SoftwareXVol. 27
Institute for High Performance Computing and Networking (IT), Fondazione Idis Città della Scienza (IT), Esaote (Italy) (IT), Institute of Biostructure and Bioimaging (IT), Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (IT), National Research Council (IT), University of Florence (IT), University of Naples Federico II (IT)
Ministero dell’Istruzione, dell’Università e della Ricerca
Openalex Percentile: Top 12%
Total Knee Arthroplasty Outcomes
2.29
DOIOpen Access PDFOpenAlex
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