Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
A survey on deep learning-based architectures for semantic segmentation on 2d images
I Ulku, E Akagündüz - Applied Artificial Intelligence, 2022 - Taylor & Francis
Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary
ability of convolutional neural networks (CNN) in creating semantic, high-level and …
ability of convolutional neural networks (CNN) in creating semantic, high-level and …
Survey on semantic segmentation using deep learning techniques
Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …
have been developed to tackle this problem ranging from autonomous vehicles, human …
Coronary angiography image segmentation based on PSPNet
X Zhu, Z Cheng, S Wang, X Chen, G Lu - Computer Methods and Programs …, 2021 - Elsevier
Purpose: Coronary artery disease (CAD) is known to have high prevalence, high disability
and mortality. The incidence and mortality of cardiovascular disease are also gradually …
and mortality. The incidence and mortality of cardiovascular disease are also gradually …
A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks
MD Jenkins, TA Carr, MI Iglesias… - 2018 26th European …, 2018 - ieeexplore.ieee.org
Deterioration of road and pavement surface conditions is an issue which directly affects the
majority of the world today. The complex structure and textural similarities of surface cracks …
majority of the world today. The complex structure and textural similarities of surface cracks …
High-level prior-based loss functions for medical image segmentation: A survey
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …
performance for supervised medical image segmentation, across various imaging modalities …
Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review
This paper presents a systematic review of the literature focused on the lung nodule
detection in chest computed tomography (CT) images. Manual detection of lung nodules by …
detection in chest computed tomography (CT) images. Manual detection of lung nodules by …
Kidney tumor semantic segmentation using deep learning: A survey of state-of-the-art
A Abdelrahman, S Viriri - Journal of imaging, 2022 - mdpi.com
Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic
procedures for early detection and diagnosis are crucial. Some difficulties with manual …
procedures for early detection and diagnosis are crucial. Some difficulties with manual …
[HTML][HTML] Uni-temporal multispectral imagery for burned area mapping with deep learning
Accurate burned area information is needed to assess the impacts of wildfires on people,
communities, and natural ecosystems. Various burned area detection methods have been …
communities, and natural ecosystems. Various burned area detection methods have been …
A review on progress in semantic image segmentation and its application to medical images
Semantic image segmentation is a popular image segmentation technique where each pixel
in an image is labeled with an object class. This technique has become a vital part of image …
in an image is labeled with an object class. This technique has become a vital part of image …