Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic
liver and tumor segmentation are of great value in clinical practice as they can reduce …

Medical Research field has been taken continuous efforts to develop an efficient method for
detecting breast cancer, but the goal has still not yet achieved. To overcome this issue, a 4D …

Lung segmentation using computed tomography (CT) images is important for diagnosing
various lung diseases. Currently, no lung segmentation method has been developed for …

Medical image segmentation plays a crucial role in diagnosing and staging diseases. It
facilitates image analysis and quantification in multiple applications, but building the right …

Brain tumor diagnosis has been a lengthy process, and automation of a process such as
brain tumor segmentation speeds up the timeline. U-Nets have been a commonly used …

Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy
boundaries and large shape variability. Although using U-Net for liver and LT segmentation …

Nuclei segmentation has great significance in biomedical applications as the preliminary
step for disease diagnosis and treatment analysis. In this study, we propose a model for …

Segmentation of brain regions affected by ischemic stroke helps to overcome the main
obstacles in modern studies of stroke visualization. Unfortunately, contemporary methods of …

Image segmentation is crucial for various research areas. Many computer vision
applications depend on segmenting images to understand the scene, such as autonomous …

Concurrent nuclear segmentation and classification in Hematoxylin & Eosin-stained
histopathology images are a crucial task in disease diagnosis and prognosis. Albeit recent …