Medical image segmentation using deep learning: A survey
Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …
papers has been presented recording the success of deep learning in the field. A …
Deep learning techniques for tumor segmentation: a review
Recently, deep learning, especially convolutional neural networks, has achieved the
remarkable results in natural image classification and segmentation. At the same time, in the …
remarkable results in natural image classification and segmentation. At the same time, in the …
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
Cross-modality deep feature learning for brain tumor segmentation
Recent advances in machine learning and prevalence of digital medical images have
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
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 …
Exploring task structure for brain tumor segmentation from multi-modality MR images
Brain tumor segmentation, which aims at segmenting the whole tumor area, enhancing
tumor core area, and tumor core area from each input multi-modality bio-imaging data, has …
tumor core area, and tumor core area from each input multi-modality bio-imaging data, has …
Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method
A Khosravanian, M Rahmanimanesh… - Computer Methods and …, 2021 - Elsevier
Abstract Background and Objective Brain tumor segmentation is a challenging issue due to
noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual …
noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual …
Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation
LI Song, KF Geoffrey, HE Kaijian - Expert Systems with Applications, 2020 - Elsevier
Liver cancer is one of the most common cancer types with high death rate. Doctors diagnose
cancer by examining the CT images, which can be time-consuming and prone to error …
cancer by examining the CT images, which can be time-consuming and prone to error …
A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images
Glioma is a relatively common brain tumor disease with high mortality rate. Humans have
been seeking a more effective therapy. In the course of treatment, the specific location of the …
been seeking a more effective therapy. In the course of treatment, the specific location of the …
Canet: Context aware network for brain glioma segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision,
progression monitoring and surgery planning. Based on deep neural networks, previous …
progression monitoring and surgery planning. Based on deep neural networks, previous …