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Clinically Interpretable Nuclei Segmentation for Robust Histopathological Image Analysis
Applied SciencesBackground/Objectives: Accurate nuclear segmentation is a fundamental step in computational pathology, enabling reliable estimation of cellularity and nuclear morphology. However, segmentation models are typically evaluated under ideal imaging conditions,
Liana Stanescu, Cosmin Stoica Spahiu
doaj +1 more source
U-net weights for proton lung MRI segmentation [PDF]
, 2018 U-net weights for proton lung MRI segmentation described in this paper: https://www.ncbi.nlm.nih.gov/pubmed ...
Nick Tustison (441144)
core +1 more source
U-net weights for human CT lung segmentation [PDF]
, 2019 U-net weights for CT human lung segmentation (background, left lung, right lung, trachea)
Nick Tustison (441144)
core +1 more source
Fruit tree canopy segmentation from UAV orthophoto maps based on a lightweight improved U-Net [PDF]
, 2023 Segmenting fruit tree canopies from drone remote sensing images is a prerequisite for achieving accurate agricultural monitoring and precision aerial spraying at the individual tree (instance) level.
Cunjia Liu (1176420) +4 more
core +2 more sources
Dense-U-net network structure. [PDF]
A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of
Xiaoli Yang (314226) +4 more
core +1 more source
Agreement ratios between renal pathologists with and without U-Net-segmented images. [PDF]
, 2022 Agreement ratios between renal pathologists with and without U-Net-segmented images.
Satoshi Hara (539722) +10 more
core +1 more source
An attempt at beating the 3D U-Net [PDF]
Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19, 2019 The U-Net is arguably the most successful segmentation architecture in the medical domain. Here we apply a 3D U-Net to the 2019 Kidney and Kidney Tumor Segmentation Challenge and attempt to improve upon it by augmenting it with residual and pre-activation residual blocks.
Isensee, Fabian, Maier-Hein, Klaus H.
openaire +3 more sources
A Bibliometric Analysis of Publications in Uremic Toxins From 1991 to 2024
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Uremic toxins are a growing area of research in nephrology, with significant implications in the progression and treatment of chronic kidney disease (CKD) and the management of end‐stage kidney disease (ESKD). This bibliometric analysis aims to evaluate the global research trends, key contributors, and the impact of publications in ...
Yuh‐Shan Ho +7 more
wiley +1 more source
Fully automated condyle segmentation using 3D convolutional neural networks
Scientific Reports, 2022 The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy.
Nayansi Jha +6 more
doaj +1 more source
U-Net-Based Models towards Optimal MR Brain Image Segmentation
Diagnostics, 2023 Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed.
Rammah Yousef +6 more
doaj +1 more source