In this paper, we focus on automatic kidneys detection in 2D abdominal computed tomography (CT) images. Identifying abdominal organs is one of the essential steps for visualization and for providing assistance in teaching, clinical training and diagnosis. It is also a key step in medical image retrieval application. However, due to gray levels similarities of adjacent organs, contrast media effect and relatively high variation of organ's positions and shapes, automatically identifying abdominal organs has always been a challenging task. In this paper, we present an original method, in a statistical framework, for fully automatic kidneys detection. It makes use of spatial and gray-levels prior models built using a set of training images. The method is tested on over 400 clinically acquired images and very promising results are obtained.
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http://dx.doi.org/10.1007/11566465_33 | DOI Listing |
Rofo
January 2025
Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany.
Multiparametric MRI is a promising technique for noninvasive structural and functional imaging of the kidneys that is gaining increasing importance in clinical research. Still, there are no standardized recommendations for analyzing the acquired images and there is a need to further evaluate the accuracy and repeatability of currently recommended MRI parameters. The aim of the study was to evaluate the test-retest repeatability of functional renal MRI parameters using different image analysis strategies.
View Article and Find Full Text PDFPhys Med
January 2025
Department of Medical Physics, Faculty of Medicine, University of Crete, P.O. Box 2208, 71003 Iraklion, Crete, Greece.
Purpose: To investigate the performance of a machine learning-based segmentation method for treatment planning of gastric cancer.
Materials And Methods: Eighteen patients planned to be irradiated for gastric cancer were studied. The target and the surrounding organs-at-risk (OARs) were manually delineated on CT scans.
J Imaging Inform Med
January 2025
Mechanical Engineering Department, Tianjin University, No. 135, Yaguan Road, Haihe Education Park, Jinnan District, Tianjin City, 300350, China.
The hybrid CNN-transformer structures harness the global contextualization of transformers with the local feature acuity of CNNs, propelling medical image segmentation to the next level. However, the majority of research has focused on the design and composition of hybrid structures, neglecting the data structure, which enhance segmentation performance, optimize resource efficiency, and bolster model generalization and interpretability. In this work, we propose a data-oriented octree inverse hierarchical order aggregation hybrid transformer-CNN (nnU-OctTN), which focuses on delving deeply into the data itself to identify and harness potential.
View Article and Find Full Text PDFCommun Med (Lond)
January 2025
Department of Dermatology, Graduate School of Medicine, Tohoku University, Sendai, Japan.
Background: Chronic kidney disease (CKD) causes progressive and irreversible damage to the kidneys. Renal biopsies are essential for diagnosing the etiology and prognosis of CKD, while accurate quantification of tubulo-interstitial injuries from whole slide images (WSIs) of renal biopsy specimens is challenging with visual inspection alone.
Methods: We develop a deep learning-based method named DLRS to quantify interstitial fibrosis and inflammatory cell infiltration as tubulo-interstitial injury scores, from WSIs of renal biopsy specimens.
Zhongguo Shi Yan Xue Ye Xue Za Zhi
December 2024
Department of Blood Transfusion, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200127, China.
Objective: To investigate and assess hemolytic transfusion reaction in patient with complex and combined anti-Fy and anti-Jk which so as to provide a safety blood transfusion strategy.
Methods: ABO/Rh blood grouping, antibody screening and identification, and Coombs' tests were performed by the routine serological methods include manual tube and automatic blood group analyzer with matching micro-column gel cards from Diagnostic Grifols and Jiangsu LIBO. The hospital information system and laboratory information system were used to collect dada on patients' blood routine tests, liver and kidney function, coagulation, cardiac function, and other clinical indicators before and after blood transfusion were analyzed and compared in conjunction with the patients' clinical manifestations.
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