Keyword search (4,164 papers available)

"Segmentation" Keyword-tagged Publications:

Title Authors PubMed ID
1 Towards user-centered interactive medical image segmentation in VR with an assistive AI agent Spiegler P; Harirpoush A; Xiao Y; 41509996
ENCS
2 MedCLIP-SAMv2: Towards universal text-driven medical image segmentation Koleilat T; Asgariandehkordi H; Rivaz H; Xiao Y; 40779830
ENCS
3 Exploring interaction paradigms for segmenting medical images in virtual reality Jones Z; Drouin S; Kersten-Oertel M; 40402355
ENCS
4 MRI as a viable alternative to CT for 3D surgical planning of Cavitary bone tumors Chae Y; Cheers GM; Kim M; Reidler P; Klein A; Fevens T; Holzapfel BM; Mayer-Wagner S; 40049253
ENCS
5 Open access segmentations of intraoperative brain tumor ultrasound images Behboodi B; Carton FX; Chabanas M; de Ribaupierre S; Solheim O; Munkvold BKR; Rivaz H; Xiao Y; Reinertsen I; 39047165
SOH
6 FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images Paul S; Patterson Z; Bouguila N; 38535151
ENCS
7 Impaired performance of rapid grip in people with Parkinson's disease and motor segmentation Rebecca J Daniels 38507858
PSYCHOLOGY
8 PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from magnetic resonance imaging Anstruther M; Rossini B; Zhang T; Liang T; Xiao Y; Fortin M; 37996857
SOH
9 Compatible-domain Transfer Learning for Breast Cancer Classification with Limited Annotated Data Shamshiri MA; Krzyzak A; Kowal M; Korbicz J; 36758326
ENCS
10 Measures of motor segmentation from rapid isometric force pulses are reliable and differentiate Parkinson's disease from age-related slowing Howard SL; Grenet D; Bellumori M; Knight CA; 35768733
PSYCHOLOGY
11 Spoken Word Segmentation in First and Second Language: When ERP and Behavioral Measures Diverge Gilbert AC; Lee JG; Coulter K; Wolpert MA; Kousaie S; Gracco VL; Klein D; Titone D; Phillips NA; Baum SR; 34603133
PSYCHOLOGY
12 Sharp U-Net: Depthwise convolutional network for biomedical image segmentation Zunair H; Ben Hamza A; 34348214
ENCS
13 LUMINOUS database: lumbar multifidus muscle segmentation from ultrasound images Belasso CJ; Behboodi B; Benali H; Boily M; Rivaz H; Fortin M; 33097024
PERFORM
14 Two-stage ultrasound image segmentation using U-Net and test time augmentation. Amiri M; Brooks R; Behboodi B; Rivaz H; 32350786
IMAGING
15 Statistical learning of multiple speech streams: A challenge for monolingual infants. Benitez VL, Bulgarelli F, Byers-Heinlein K, Saffran JR, Weiss DJ 31444822
CONCORDIA
16 High resolution atlas of the venous brain vasculature from 7 T quantitative susceptibility maps. Huck J, Wanner Y, Fan AP, Jäger AT, Grahl S, Schneider U, Villringer A, Steele CJ, Tardif CL, Bazin PL, Gauthier CJ 31278570
PSYCHOLOGY
17 The first MICCAI challenge on PET tumor segmentation. Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky F, Girard P, Glatard T, Kain M, Yao Y, Barillot C, Kirov A, Visvikis D 29268169
IMAGING
18 A dataset of multi-contrast population-averaged brain MRI atlases of a Parkinson׳s disease cohort. Xiao Y, Fonov V, Chakravarty MM, Beriault S, Al Subaie F, Sadikot A, Pike GB, Bertrand G, Collins DL 28491942
PERFORM

 

Title:MRI as a viable alternative to CT for 3D surgical planning of Cavitary bone tumors
Authors:Chae YCheers GMKim MReidler PKlein AFevens THolzapfel BMMayer-Wagner S
Link:https://pubmed.ncbi.nlm.nih.gov/40049253/
DOI:10.1016/j.mri.2025.110369
Publication:Magnetic resonance imaging
Keywords:Bone defectsCTImagingMRIPreoperativeSegmentationTumors
PMID:40049253 Category: Date Added:2025-03-07
Dept Affiliation: ENCS
1 Department of Orthopaedics and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Ludwig Maximilian University (LMU) University Hospital, LMU Munich, 81377 Munich, Germany.
2 Department of Orthopaedics and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Ludwig Maximilian University (LMU) University Hospital, LMU Munich, 81377 Munich, Germany.. Electronic address: giles.cheers@med.uni-muenchen.de.
3 Department of Radiology, LMU University Hospital, LMU Munich, 81377 Munich, Germany.
4 Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada.
5 Department of Orthopaedics and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Ludwig Maximilian University (LMU) University Hospital, LMU Munich, 81377 Munich, Germany.. Electronic address: susanne.mayer@med.uni-muenchen.de.

Description:

Cavitary bone defects, defined as a volumetric loss of native bone tissue, require accurate preoperative imaging for treatment planning. While CT (computed tomography) has traditionally been the gold standard for segmentation due to its superior resolution of cortical bone, MRI (magnetic resonance imaging) offers unique advantages, particularly in visualizing the soft tissue-bone interface. Furthermore, MRI eliminates the ionizing radiation associated with CT, making it an advantageous alternative, especially in the management of benign and low-grade malignant bone tumors. Despite these advantages, MRI's inherently lower spatial resolution may introduce artifacts, which can complicate segmentation accuracy. This study evaluates the feasibility of MRI as a viable alternative to CT in the preoperative planning of cavitary bone defect treatment. We analyzed CT and MRI scans from 80 patients with benign and locally aggressive primary bone tumors, generating three-dimensional models through manual segmentation in Mimics, validated using Geomagic Control X. Volumetric differences between the CT- and MRI-derived models were assessed using the Wilcoxon signed-rank test and paired t-test. The mean volumetric difference between MRI and CT scans was 2.68 ± 1.44 %, which was not statistically significant (p = 0.15). Additionally, multiple regression analysis examining sex, age, and diagnosis revealed no significant differences in the 3D model volumes derived from the two imaging modalities (sex: p = 0.51, age: p = 0.98, diagnosis: p = 0.50). These results support MRI-based segmentation as a reliable, radiation-free alternative to CT, particularly when precise delineation of soft tissue boundaries is critical for surgical planning.





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