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A review on the use of deep learning for medical images segmentation
Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …
images. They have been used extensively for medical image segmentation as the first and …
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks
Variations in the shape and appearance of anatomical structures in medical images are
often relevant radiological signs of disease. Automatic tools can help automate parts of this …
often relevant radiological signs of disease. Automatic tools can help automate parts of this …
Deep learning classification for crop types in north dakota
Recently, agricultural remote sensing community has endeavored to utilize the power of
artificial intelligence (AI). One important topic is using AI to make the map** of crops more …
artificial intelligence (AI). One important topic is using AI to make the map** of crops more …
Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis: Neural Network-based Methods for Liver Semantic …
JC Delmoral, JM RS Tavares - Journal of Medical Systems, 2024 - Springer
The use of artificial intelligence (AI) in the segmentation of liver structures in medical images
has become a popular research focus in the past half-decade. The performance of AI tools in …
has become a popular research focus in the past half-decade. The performance of AI tools in …
Multimodal graph attention network for COVID-19 outcome prediction
When dealing with a newly emerging disease such as COVID-19, the impact of patient-and
disease-specific factors (eg, body weight or known co-morbidities) on the immediate course …
disease-specific factors (eg, body weight or known co-morbidities) on the immediate course …
Segmentation of skeleton and organs in whole-body CT images via iterative trilateration
Whole body oncological screening using CT images requires a good anatomical localisation
of organs and the skeleton. While a number of algorithms for multi-organ localisation have …
of organs and the skeleton. While a number of algorithms for multi-organ localisation have …
Creating a large-scale silver corpus from multiple algorithmic segmentations
M Krenn, M Dorfer, OA Jiménez del Toro… - … Vision: Algorithms for …, 2016 - Springer
Currently, increasingly large medical imaging data sets become available for research and
are analysed by a range of algorithms segmenting anatomical structures automatically and …
are analysed by a range of algorithms segmenting anatomical structures automatically and …
Unsupervised identification of clinically relevant clusters in routine imaging data
A key question in learning from clinical routine imaging data is whether we can identify
coherent patterns that re-occur across a population, and at the same time are linked to …
coherent patterns that re-occur across a population, and at the same time are linked to …
Non-contrast CT liver segmentation using CycleGAN data augmentation from contrast enhanced CT
C Song, B He, H Chen, S Jia, X Chen, F Jia - Interpretable and Annotation …, 2020 - Springer
Non-contrast CT is often preferred in clinical screening while segmentation of such CT data
is more challenging due to the low contrast in tissue boundaries and scarce supervised …
is more challenging due to the low contrast in tissue boundaries and scarce supervised …