Current and emerging trends in medical image segmentation with deep learning
PH Conze, G Andrade-Miranda… - … on Radiation and …, 2023 - ieeexplore.ieee.org
In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …
learning has experienced a widespread interest in medical image analysis. Remarkably …
[HTML][HTML] A review on brain tumor segmentation based on deep learning methods with federated learning techniques
Brain tumors have become a severe medical complication in recent years due to their high
fatality rate. Radiologists segment the tumor manually, which is time-consuming, error …
fatality rate. Radiologists segment the tumor manually, which is time-consuming, error …
Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Semantic segmentation of brain tumors is a fundamental medical image analysis task
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …
Deep learning based brain tumor segmentation: a survey
Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
Xnet: Wavelet-based low and high frequency fusion networks for fully-and semi-supervised semantic segmentation of biomedical images
Fully-and semi-supervised semantic segmentation of biomedical images have been
advanced with the development of deep neural networks (DNNs). So far, however, DNN …
advanced with the development of deep neural networks (DNNs). So far, however, DNN …
WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
Whole abdominal organ segmentation is important in diagnosing abdomen lesions,
radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from …
radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from …
A fully automated multimodal MRI-based multi-task learning for glioma segmentation and IDH genotyping
The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma
segmentation are important tasks for computer-aided diagnosis using preoperative …
segmentation are important tasks for computer-aided diagnosis using preoperative …
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 …
RFNet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation
Most existing brain tumor segmentation methods usually exploit multi-modal magnetic
resonance imaging (MRI) images to achieve high segmentation performance. However, the …
resonance imaging (MRI) images to achieve high segmentation performance. However, the …
MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images
Y Cao, W Zhou, M Zang, D An, Y Feng, B Yu - … Signal Processing and …, 2023 - Elsevier
More than half of brain tumors are malignant tumors, so there is a need for fast and accurate
segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) images …
segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) images …