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 …
[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 …
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
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 …
Advances in Deep Learning Models for Resolving Medical Image Segmentation Data Scarcity Problem: A Topical Review
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 …
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 …