Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT

Article Optics

Semantic segmentation using deep learning to extract total extraocular muscles and optic nerve from orbital computed tomography images

Fubao Zhu et al.

Summary: A semantic segmentation method using a 3D neural network was developed to automatically extract total extraocular muscles (EOM) and optic nerve (ON) from orbital CT images, showing potential for clinical application in the diagnosis and prognosis of thyroid-associated ophthalmopathy (TAO).

OPTIK (2021)

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Article Computer Science, Artificial Intelligence

Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images

Chen Zhao et al.

Summary: This paper proposes an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features for assisting radiologists in COVID-19 diagnosis. Experimental results demonstrate high accuracy and reliability in segmentation and diagnosis using sophisticated statistical models and radiomic features with high interpretability.

PATTERN RECOGNITION (2021)

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Article Computer Science, Artificial Intelligence

Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images

Nan Mu et al.

Summary: The paper proposes a PCPLP network for automatic segmentation of COVID-19-caused pneumonia infections in CT images, incorporating global perception and local optimization. By integrating features and designing different modules, the segmentation accuracy and efficiency have been improved significantly.

PATTERN RECOGNITION (2021)

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Article Computer Science, Interdisciplinary Applications

SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography

Jinke Wang et al.

Summary: Liver segmentation is crucial for liver cancer diagnosis and surgical planning. In this paper, a modified U-Net framework incorporating SE, ASPP, and residual learning techniques was proposed for accurate and robust liver CT segmentation, showing higher accuracy compared to other closely related models on LiTS17 and SLiver07 datasets.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

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Article Computer Science, Interdisciplinary Applications