[HTML][HTML] Brain tumor detection in MR image using superpixels, principal component analysis and template based K-means clustering algorithm
In the present era, human brain tumor is the extremist dangerous and devil to the human
being that leads to certain death. Furthermore, the brain tumor arises more complexity of …
being that leads to certain death. Furthermore, the brain tumor arises more complexity of …
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging--Mini Review, Comparison and Perspectives
Magnetic Resonance Imaging (MRI) is a vital component of medical imaging. When
compared to other image modalities, it has advantages such as the absence of radiation …
compared to other image modalities, it has advantages such as the absence of radiation …
Encoder modified U-net and feature pyramid network for multi-class segmentation of cardiac magnetic resonance images
Cardiovascular diseases are leading cause of death worldwide. Timely and accurate
detection of disease is required to reduce load on healthcare system and number of deaths …
detection of disease is required to reduce load on healthcare system and number of deaths …
Deep Learning–based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy
S Nigro, M Filardi, B Tafuri, M Nicolardi… - Radiology: Artificial …, 2024 - pubs.rsna.org
Purpose To develop a fast and fully automated deep learning (DL)–based method for the
MRI planimetric segmentation and measurement of the brainstem and ventricular structures …
MRI planimetric segmentation and measurement of the brainstem and ventricular structures …
[HTML][HTML] Artificial intelligence for brain neuroanatomical segmentation in magnetic resonance imaging: A literature review
M Andrews, A Di Ieva - Journal of Clinical Neuroscience, 2025 - Elsevier
Purpose This literature review aims to synthesise current research on the application of
artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in …
artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in …
Automated ventricular segmentation and shunt failure detection using convolutional neural networks
While ventricular shunts are the main treatment for adult hydrocephalus, shunt malfunction
remains a common problem that can be challenging to diagnose. Computer vision-derived …
remains a common problem that can be challenging to diagnose. Computer vision-derived …
Computer-based segmentation of cancerous tissues in biomedical images using enhanced deep learning model
In this manuscript, we proposed an automatic segmentation method which was developed
using the depth-wise separable convolution with bottleneck connections. The data were …
using the depth-wise separable convolution with bottleneck connections. The data were …
An augmented deep learning network with noise suppression feature for efficient segmentation of magnetic resonance images
The segmentation of cardiac MR images requires extensive attention as it needs a high level
of care and analysis for the diagnosis of affected part. The advent of deep learning …
of care and analysis for the diagnosis of affected part. The advent of deep learning …
Classification of brain tumours from MR images with an enhanced deep learning approach using densely connected convolutional network
Brain cancer is one of the most leading causes of death in human beings. There are different
types of tumours affecting the brain and early diagnosis of them increases the survival rate …
types of tumours affecting the brain and early diagnosis of them increases the survival rate …
AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH)
patients, using machine learning methods, we aim to establish a multimodal and high …
patients, using machine learning methods, we aim to establish a multimodal and high …