2.5 D lightweight RIU-Net for automatic liver and tumor segmentation from CT
P Lv, J Wang, H Wang - Biomedical signal processing and control, 2022 - Elsevier
Purpose One critical factor that restricts the clinical application of computer-aided liver and
tumor segmentation is the method's high complexity and low accuracy. Overcoming this …
tumor segmentation is the method's high complexity and low accuracy. Overcoming this …
Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN
The accurate segmentation and identification of vertebrae presents the foundations for spine
analysis including fractures, malfunctions and other visual insights. The large-scale …
analysis including fractures, malfunctions and other visual insights. The large-scale …
An automated deep learning approach for spine segmentation and vertebrae recognition using computed tomography images
Spine image analysis is based on the accurate segmentation and vertebrae recognition of
the spine. Several deep learning models have been proposed for spine segmentation and …
the spine. Several deep learning models have been proposed for spine segmentation and …
Ndg-cam: Nuclei detection in histopathology images with semantic segmentation networks and grad-cam
Nuclei identification is a fundamental task in many areas of biomedical image analysis
related to computational pathology applications. Nowadays, deep learning is the primary …
related to computational pathology applications. Nowadays, deep learning is the primary …
Focal dice loss-based V-Net for liver segments classification
Liver segmentation is a crucial step in surgical planning from computed tomography scans.
The possibility to obtain a precise delineation of the liver boundaries with the exploitation of …
The possibility to obtain a precise delineation of the liver boundaries with the exploitation of …
Lung segmentation and characterization in COVID-19 patients for assessing pulmonary thromboembolism: an approach based on deep learning and radiomics
The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of
computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients …
computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients …
3D MRU-Net: A novel mobile residual U-Net deep learning model for spine segmentation using computed tomography images
The efficient and accurate segmentation of the spine shows the basis of spine analysis
including visual insights, malfunctions, and fractures. The spine is made up of 33 vertebrae …
including visual insights, malfunctions, and fractures. The spine is made up of 33 vertebrae …
A fusion biopsy framework for prostate cancer based on deformable superellipses and nnU-Net
In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with
transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the …
transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the …
PGFC-Net: Parallel-Encoding Gaussian Feature Coordination-Enhanced Network for Accurate 3D Hepatic Vessel and Inferior Vena Cava Segmentation
SY Jiang, JY Bao, M Yue, K Chen, J Wang - Neurocomputing, 2025 - Elsevier
Segmentation and visualisation of the hepatic vessels and inferior vena cava in three-
dimensional computed tomography (CT) images are crucial for computer-aided diagnosis …
dimensional computed tomography (CT) images are crucial for computer-aided diagnosis …
Automatic hepatic vessels segmentation using rorpo vessel enhancement filter and 3D V-net with variant dice loss function
P Svobodova, K Sethia, P Strakos, A Varysova - Applied Sciences, 2022 - mdpi.com
The segmentation of hepatic vessels is crucial for liver surgical planning. It is also a
challenging task because of its small diameter. Hepatic vessels are often captured in images …
challenging task because of its small diameter. Hepatic vessels are often captured in images …