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Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis.
J Imaging
December 2024
Technology Department, CERN, 1211 Geneva, Switzerland.
Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH).
View Article and Find Full Text PDFImaging Findings, Complications, and Mimics after Common and Advanced Dental Procedures.
Radiographics
February 2025
From the Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba 271-8587, Japan (K.I., K.O., T.K.); Department of Diagnostic Radiology, National Cancer Center Hospital East, Chiba, Japan (H.K.); Department of Radiology, VA Boston Health Care System, Boston, Mass (V.C.A.A.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.S.).
Various new dental treatment methods have been introduced in dental clinics, and many new materials have been used in recent years for dental treatments. Dentistry is divided into several specialties, each offering unique treatments, such as endodontics, implantology, oral surgery, and orthodontics. CT and MR images after dental treatment reveal a variety of hard- and soft-tissue changes and dental materials, which often cause image artifacts.
View Article and Find Full Text PDFComparing synthetic mammograms based on wide-angle digital breast tomosynthesis with digital mammograms.
J Med Imaging (Bellingham)
January 2025
Lund University, Department of Translational Medicine, Medical Radiation Physics, Malmö, Sweden.
Purpose: We aim to investigate the characteristics and evaluate the performance of synthetic mammograms (SMs) based on wide-angle digital breast tomosynthesis (DBT) compared with digital mammography (DM).
Approach: Fifty cases with both synthetic and digital mammograms were selected from the Malmö Breast Tomosynthesis Screening Trial. They were categorized into five groups consisting of normal cases and recalled cases with false-positive and true-positive findings from DM and DBT only.
Diagnostic performance of CT for extrarenal fat invasion in renal cell carcinoma: a meta-analysis and systematic review.
Insights Imaging
January 2025
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Objectives: Renal cell carcinoma (RCC) with extrarenal fat (perinephric or renal sinus fat) invasion is the main evidence for the T3a stage. Currently, computed tomography (CT) is still the primary modality for staging RCC. This study aims to determine the diagnostic performance of CT in RCC patients with extrarenal fat invasion.
View Article and Find Full Text PDFEvaluating the Impact of Changes in AI-derived Case Scores over Time on Digital Breast Tomosynthesis Screening Outcomes.
Radiol Artif Intell
January 2025
Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104.
Purpose To evaluate the change in DBT-AI (digital breast tomosynthesis-artificial intelligence) case scores over sequential screens. Materials and Methods This retrospective review included 21,108 female patients (mean age, 58.1 ± [SD] 11.
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