FABnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer
Perineural invasion (PNI), lymphovascular invasion (LVI) and tumor angiogenesis have
strong correlation with cancer recurrence, metastasis and poor patient survival. The accurate …
strong correlation with cancer recurrence, metastasis and poor patient survival. The accurate …
Curv‐Net: curvilinear structure segmentation network based on selective kernel and multi‐Bi‐ConvLSTM
Purpose Accurately segmenting curvilinear structures, for example, retinal blood vessels or
nerve fibers, in the medical image is essential to the clinical diagnosis of many diseases …
nerve fibers, in the medical image is essential to the clinical diagnosis of many diseases …
3D dilated multi-fiber network for real-time brain tumor segmentation in MRI
Brain tumor segmentation plays a pivotal role in medical image processing. In this work, we
aim to segment brain MRI volumes. 3D convolution neural networks (CNN) such as 3D U …
aim to segment brain MRI volumes. 3D convolution neural networks (CNN) such as 3D U …
FD-FCN: 3D fully dense and fully convolutional network for semantic segmentation of brain anatomy
B Yang, W Zhang - arXiv preprint arXiv:1907.09194, 2019 - arxiv.org
In this paper, a 3D patch-based fully dense and fully convolutional network (FD-FCN) is
proposed for fast and accurate segmentation of subcortical structures in T1-weighted …
proposed for fast and accurate segmentation of subcortical structures in T1-weighted …
Segmentation of peripheral nerves from magnetic resonance neurography: a fully-automatic, deep learning-based approach
F Balsiger, C Steindel, M Arn, B Wagner… - Frontiers in …, 2018 - frontiersin.org
Diagnosis of peripheral neuropathies relies on neurological examinations, electrodiagnostic
studies, and since recently magnetic resonance neurography (MRN). The aim of this study …
studies, and since recently magnetic resonance neurography (MRN). The aim of this study …
Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN
J Sun, Y Peng, Y Guo, D Li - Neurocomputing, 2021 - Elsevier
Segmentation of multimodal brain tissues from 3D medical images is of great significance for
brain diagnosis. It is required to create an automated and accurate segmentation based on …
brain diagnosis. It is required to create an automated and accurate segmentation based on …
Improved segmentation of basal ganglia from MR images using convolutional neural network with crossover-typed skip connection
T Sugino, T Kin, N Saito, Y Nakajima - International journal of computer …, 2024 - Springer
Purpose Accurate and automatic segmentation of basal ganglia from magnetic resonance
(MR) images is important for diagnosis and treatment of various brain disorders. However …
(MR) images is important for diagnosis and treatment of various brain disorders. However …
Deep cortical vessel segmentation driven by data augmentation with neural image analogy
During a craniotomy, a bone flap is temporarily removed from the skull to reveal the brain for
surgery. The cortical vessels located at the surface of the brain are considered strong …
surgery. The cortical vessels located at the surface of the brain are considered strong …
ACEnet: Anatomical context-encoding network for neuroanatomy segmentation
Segmentation of brain structures from magnetic resonance (MR) scans plays an important
role in the quantification of brain morphology. Since 3D deep learning models suffer from …
role in the quantification of brain morphology. Since 3D deep learning models suffer from …
Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging
Accurate brain tissue segmentation in magnetic resonance imaging (MRI) has attracted the
attention of medical doctors and researchers since variations in tissue volume and shape …
attention of medical doctors and researchers since variations in tissue volume and shape …