Interactive segmentation is a crucial research area in medical image analysis aiming to
boost the efficiency of costly annotations by incorporating human feedback. This feedback …

Accurate and massive medical image annotation data is crucial for diagnosis, surgical
planning, and deep learning in the development of medical images. However, creating large …

In medical imaging, the last decade has witnessed a remarkable increase in the availability
and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant …

Accurate organ segmentation in ultrasound (US) images remains challenging because such
images have inhomogeneous intensity distributions in their regions of interest (ROIs) and …

The active contour model (ACM) plays a paramount part in grasping visual properties of
images and exacting targets of interest. It is overwhelming hardship for traditional ACMs to …

Lung images often have the characteristics of strong noise, uneven grayscale distribution,
and complex pathological structures, which makes lung image segmentation a challenging …

This paper introduces an automatic segmentation system designed for precise outlining of
the pulmonary area within 3D computed tomography (CT) scans, utilizing a combination of …

Diagnosing lung diseases accurately and promptly is essential for effectively managing this
significant public health challenge on a global scale. This paper introduces a new …

Abstract Coronavirus disease 2019 (COVID-19) has spread globally in early 2020, leading
to a new health crisis. Automatic segmentation of lung infections from computed tomography …

Aiming at the problems of difficult segmentation and inaccurate segmentation of small
lesions in lung Computed Tomography (CT) images, a multi-scale feature fusion deep …