A review on deep-learning algorithms for fetal ultrasound-image analysis
Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US)
fetal images. A number of survey papers in the field is today available, but most of them are …
fetal images. A number of survey papers in the field is today available, but most of them are …
[HTML][HTML] Deep neural network pulmonary nodule segmentation methods for CT images: Literature review and experimental comparisons
L Zhi, W Jiang, S Zhang, T Zhou - Computers in Biology and Medicine, 2023 - Elsevier
Automatic and accurate segmentation of pulmonary nodules in CT images can help
physicians perform more accurate quantitative analysis, diagnose diseases, and improve …
physicians perform more accurate quantitative analysis, diagnose diseases, and improve …
Anatomy-aided deep learning for medical image segmentation: a review
Deep learning (DL) has become widely used for medical image segmentation in recent
years. However, despite these advances, there are still problems for which DL-based …
years. However, despite these advances, there are still problems for which DL-based …
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
Anatomical segmentation is a fundamental task in medical image computing, generally
tackled with fully convolutional neural networks which produce dense segmentation masks …
tackled with fully convolutional neural networks which produce dense segmentation masks …
Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios
Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is
indispensable for the comprehensive diagnosis and treatment of spinal diseases. However …
indispensable for the comprehensive diagnosis and treatment of spinal diseases. However …
An Anatomy-and Topology-Preserving Framework for Coronary Artery Segmentation
Coronary artery segmentation is critical for coronary artery disease diagnosis but
challenging due to its tortuous course with numerous small branches and inter-subject …
challenging due to its tortuous course with numerous small branches and inter-subject …
Saliency and ballness driven deep learning framework for cell segmentation in bright field microscopic images
SB Asha, G Gopakumar… - Engineering Applications of …, 2023 - Elsevier
Cell segmentation is the most significant task in microscopic image analysis as it facilitates
differential cell counting and analysis of sub-cellular structures for diagnosing …
differential cell counting and analysis of sub-cellular structures for diagnosing …
A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging
Automated medical image segmentation is an essential task to aid/speed up diagnosis and
treatment procedures in clinical practices. Deep convolutional neural networks have …
treatment procedures in clinical practices. Deep convolutional neural networks have …
Advances in Deep Learning Models for Resolving Medical Image Segmentation Data Scarcity Problem: A Topical Review
AK Upadhyay, AK Bhandari - Archives of Computational Methods in …, 2024 - Springer
Deep learning (DL) methods have recently become state-of-the-art in most automated
medical image segmentation tasks. Some of the biggest challenges in this field are related …
medical image segmentation tasks. Some of the biggest challenges in this field are related …
[HTML][HTML] Unfolding explainable AI for brain tumor segmentation
Brain tumor segmentation (BTS) has been studied from handcrafted engineered features to
conventional machine learning (ML) methods, followed by the cutting-edge deep learning …
conventional machine learning (ML) methods, followed by the cutting-edge deep learning …