Purpose: To propose a manual segmentation method for individual quadriceps femoris (QF) muscles and to test its reliability for muscle volume estimation.
Materials And Methods: Images were acquired every 5 mm along the thigh using a 3T MRI scanner on 10 young (mean age: 25 years) and 10 older (mean age: 75 years) adults using a three-point 3D Dixon sequence. In each slice, anatomical cross-sectional areas of the individual quadriceps muscles of the dominant leg were outlined by two operators working independently. Differences between operators were assessed by means of Bland-Altman plots and intraclass correlation coefficients (ICC). This study was approved by the local Ethics Committee.
Results: Precise delimitation of individual muscles along the femur often remains challenging, particularly near their insertion areas where some muscles may be partially or totally fused. There was, however, an excellent interoperator segmentation reliability despite a systematic significant difference between operators (ICC > 0.99), mainly due to delineation divergences. Considering all subjects and muscles, differences between operators were all lower than 4.4%.
Conclusion: This work has demonstrated the excellent reliability of manual segmentation to assess cross-sectional areas and therefore the volume of individual QF muscles using MRI. It may serve as a basis for a future segmentation consensus of the QF muscles.
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http://dx.doi.org/10.1002/jmri.24370 | DOI Listing |
J Neurosurg
January 2025
1Department of Neurosurgery, St. Olav's University Hospital, Trondheim, Norway.
Objective: The extent of resection (EOR) and postoperative residual tumor (RT) volume are prognostic factors in glioblastoma. Calculations of EOR and RT rely on accurate tumor segmentations. Raidionics is an open-access software that enables automatic segmentation of preoperative and early postoperative glioblastoma using pretrained deep learning models.
View Article and Find Full Text PDFPLoS One
January 2025
Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.
View Article and Find Full Text PDFInt J Numer Method Biomed Eng
January 2025
Dipartimento di Scienze Chirurgiche Odontostomatologiche e Materno-Infantili, Università di Verona, Verona, Italy.
Accurate reconstruction of the right heart geometry and motion from time-resolved medical images is crucial for diagnostic enhancement and computational analysis of cardiac blood dynamics. Commonly used segmentation and/or reconstruction techniques, exclusively relying on short-axis cine-MRI, lack precision in critical regions of the right heart, such as the ventricular base and the outflow tract, due to its unique morphology and motion. Furthermore, the reconstruction procedure is time-consuming and necessitates significant manual intervention for generating computational domains.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Radiation Oncology, University Medical Centre Freiburg, Robert-Koch Straße 3, 79106, Freiburg, Germany.
Purpose: Prostate-specific membrane-antigen positron emission tomography (PSMA PET) is a promising candidate for non-invasive characterization of prostate cancer (PCa). This study evaluated whether PET with tracers [Ga]Ga-PSMA-11 or [F]PSMA-1007 is capable to depict intratumour heterogeneity of histological PSMA expression.
Methods: Thirty-five patients with biopsy-proven primary PCa without evidence of metastatic disease nor prior interventions were prospectively enrolled.
An assessment scheme is proposed to evaluate GBM gross tumor core and T2-FLAIR hyper-intensity segmentations on preoperative multicentric MR images as a function of tumor morphology and MRI characteristics. 74 gross tumor core and T2-FLAIR hyper-intensity BraTS-Toolkit and DeepBraTumIA automatic segmentations, and 42 gross tumor core neurosurgeon manual segmentations were accordingly evaluated. Brats-Toolkit and DeepBraTumIA generally provide accurate segmentations, particularly for the most common round-shaped or well-demarked tumors, where: (1) gross tumor segmentation correctly includes necrosis and contrast enhanced tumor in 100% and 97.
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