Structure correction for robust volume segmentation in presence of tumors
CNN based lung segmentation models in absence of diverse training dataset fail to segment
lung volumes in presence of severe pathologies such as large masses, scars, and tumors …
lung volumes in presence of severe pathologies such as large masses, scars, and tumors …
Automated volumetric lung segmentation of thoracic CT images using fully convolutional neural network
M Negahdar, D Beymer… - Medical Imaging 2018 …, 2018 - spiedigitallibrary.org
Deep Learning models such as Convolutional Neural Networks (CNNs) have achieved state-
of-the-art performance in 2D medical image analysis. In clinical practice; however, most …
of-the-art performance in 2D medical image analysis. In clinical practice; however, most …
Learning to segment the lung volume from CT scans based on semi-automatic ground-truth
Lung volume segmentation is a key step in the design of Computer-Aided Diagnosis
systems for automated lung pathology analysis. However, isolating the lung from CT …
systems for automated lung pathology analysis. However, isolating the lung from CT …
[HTML][HTML] Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation
Accurate lung nodule segmentation from computed tomography (CT) images is of great
importance for image-driven lung cancer analysis. However, the heterogeneity of lung …
importance for image-driven lung cancer analysis. However, the heterogeneity of lung …
An effective approach for CT lung segmentation using mask region-based convolutional neural networks
Q Hu, LFF Souza, GB Holanda, SSA Alves… - Artificial intelligence in …, 2020 - Elsevier
Computer vision systems have numerous tools to assist in various medical fields, notably in
image diagnosis. Computed tomography (CT) is the principal imaging method used to assist …
image diagnosis. Computed tomography (CT) is the principal imaging method used to assist …
Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset
Background Lung segmentation constitutes a critical procedure for any clinical-decision
supporting system aimed to improve the early diagnosis and treatment of lung diseases …
supporting system aimed to improve the early diagnosis and treatment of lung diseases …
Fully automated lung lobe segmentation in volumetric chest CT with 3D U-Net: validation with intra-and extra-datasets
Lung lobe segmentation in chest CT has been used for the analysis of lung functions and
surgical planning. However, accurate lobe segmentation is difficult as 80% of patients have …
surgical planning. However, accurate lobe segmentation is difficult as 80% of patients have …
Robust segmentation of challenging lungs in CT using multi-stage learning and level set optimization
Automatic segmentation of lung tissue in thoracic CT scans is useful for diagnosis and
treatment planning of pulmonary diseases. Unlike healthy lung tissue that is easily …
treatment planning of pulmonary diseases. Unlike healthy lung tissue that is easily …
Lung CT Image Segmentation via Dilated U-Net Model and Multi-scale Gray Correlation-Based Approach
C Liu, M Pang - Circuits, Systems, and Signal Processing, 2024 - Springer
Lung segmentation is a prerequisite for lung cancer diagnosis with computer-aided
diagnosis systems. However, correct lung segmentation is a challenging task due to image …
diagnosis systems. However, correct lung segmentation is a challenging task due to image …
A lung dense deep convolution neural network for robust lung parenchyma segmentation
Y Chen, Y Wang, F Hu, D Wang - IEEE Access, 2020 - ieeexplore.ieee.org
Lung parenchyma segmentation is the prerequisite for an automatic diagnosis system to
analyze lung CT (computed tomography) images. However, traditional lung segmentation …
analyze lung CT (computed tomography) images. However, traditional lung segmentation …