Anthony Paproki1, Craig Engstrom2, Mark Strudwick3, Katharine J Wilson4, Rachel K Surowiec4, Charles Ho4, Stuart Crozier3, Jurgen Fripp5. 1. Australian e-Health Research Centre, CSIRO, Level 5 UQ Health Sciences Building, Royal Brisbane and Women's Hospital, Herston, Queensland 4029, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Queensland 4072, Australia; The Australian e-Health Research Centre, CSIRO, Queensland, Australia. Electronic address: anthony.paproki@uqconnect.edu.au. 2. School of Human Movement Studies, The University of Queensland, St Lucia, Queensland 4072, Australia. 3. School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Queensland 4072, Australia. 4. Steadman Philippon Research Institute, 181 West Meadow Drive, Suite 1000, Vail, CO 81657, USA. 5. The Australian e-Health Research Centre, CSIRO, Queensland, Australia.
Abstract
RATIONALE AND OBJECTIVES: This study aimed to evaluate the accuracy of an automated method for segmentation and T2 mapping of the medial meniscus (MM) and lateral meniscus (LM) in clinical magnetic resonance images from patients with acute knee injury. MATERIALS AND METHODS: Eighty patients scheduled for surgery of an anterior cruciate ligament or meniscal injury underwent magnetic resonance imaging of the knee (multiplanar two-dimensional [2D] turbo spin echo [TSE] or three-dimensional [3D]-TSE examinations, T2 mapping). Each meniscus was automatically segmented from the 2D-TSE (composite volume) or 3D-TSE images, auto-partitioned into anterior, mid, and posterior regions, and co-registered onto the T2 maps. The Dice similarity index (spatial overlap) was calculated between automated and manual segmentations of 2D-TSE (15 patients), 3D-TSE (16 patients), and corresponding T2 maps (31 patients). Pearson and intraclass correlation coefficients (ICC) were calculated between automated and manual T2 values. T2 values were compared (Wilcoxon rank sum tests) between torn and non-torn menisci for the subset of patients with both manual and automated segmentations to compare statistical outcomes of both methods. RESULTS: The Dice similarity index values for the 2D-TSE, 3D-TSE, and T2 map volumes, respectively, were 76.4%, 84.3%, and 75.2% for the MM and 76.4%, 85.1%, and 76.1% for the LM. There were strong correlations between automated and manual T2 values (rMM = 0.95, ICCMM = 0.94; rLM = 0.97, ICCLM = 0.97). For both the manual and the automated methods, T2 values were significantly higher in torn than in non-torn MM for the full meniscus and its subregions (P < .05). Non-torn LM had higher T2 values than non-torn MM (P < .05). CONCLUSIONS: The present automated method offers a promising alternative to manual T2 mapping analyses of the menisci and a considerable advance for integration into clinical workflows.
RATIONALE AND OBJECTIVES: This study aimed to evaluate the accuracy of an automated method for segmentation and T2 mapping of the medial meniscus (MM) and lateral meniscus (LM) in clinical magnetic resonance images from patients with acute knee injury. MATERIALS AND METHODS: Eighty patients scheduled for surgery of an anterior cruciate ligament or meniscal injury underwent magnetic resonance imaging of the knee (multiplanar two-dimensional [2D] turbo spin echo [TSE] or three-dimensional [3D]-TSE examinations, T2 mapping). Each meniscus was automatically segmented from the 2D-TSE (composite volume) or 3D-TSE images, auto-partitioned into anterior, mid, and posterior regions, and co-registered onto the T2 maps. The Dice similarity index (spatial overlap) was calculated between automated and manual segmentations of 2D-TSE (15 patients), 3D-TSE (16 patients), and corresponding T2 maps (31 patients). Pearson and intraclass correlation coefficients (ICC) were calculated between automated and manual T2 values. T2 values were compared (Wilcoxon rank sum tests) between torn and non-torn menisci for the subset of patients with both manual and automated segmentations to compare statistical outcomes of both methods. RESULTS: The Dice similarity index values for the 2D-TSE, 3D-TSE, and T2 map volumes, respectively, were 76.4%, 84.3%, and 75.2% for the MM and 76.4%, 85.1%, and 76.1% for the LM. There were strong correlations between automated and manual T2 values (rMM = 0.95, ICCMM = 0.94; rLM = 0.97, ICCLM = 0.97). For both the manual and the automated methods, T2 values were significantly higher in torn than in non-torn MM for the full meniscus and its subregions (P < .05). Non-torn LM had higher T2 values than non-torn MM (P < .05). CONCLUSIONS: The present automated method offers a promising alternative to manual T2 mapping analyses of the menisci and a considerable advance for integration into clinical workflows.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Susanne M Eijgenraam; Akshay S Chaudhari; Max Reijman; Sita M A Bierma-Zeinstra; Brian A Hargreaves; Jos Runhaar; Frank W J Heijboer; Garry E Gold; Edwin H G Oei Journal: Eur Radiol Date: 2019-12-16 Impact factor: 5.315