The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution
of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is …

Many known supervised deep learning methods for medical image segmentation suffer an
expensive burden of data annotation for model training. Recently, few-shot segmentation …

Medical image segmentation is one of the crucial tasks in diagnosis as well as pre-surgery.
Recently, deep learning has significantly contributed to improving the efficiency of medical …

Recently, deep learning, especially convolutional neural networks, has achieved the
remarkable results in natural image classification and segmentation. At the same time, in the …

While cross-sectional imaging has seen continuous progress and plays an undiscussed
pivotal role in the diagnostic management and treatment planning of patients with rectal …

Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance Images (MRI)
is a crucial prerequisite for NPC radiotherapy. However, manual segmenting of NPC is time …

Intracranial aneurysm (IA) is an enormous threat to human health, which often results in
nontraumatic subarachnoid hemorrhage or dismal prognosis. Diagnosing IAs on commonly …

Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of
consensus on how to assess and validate auto-contouring systems currently limits clinical …

Background and objective Segmentation of rectal cancerous regions using 2D Magnetic
Resonance Imaging (MRI) images is a critical step in radiation therapy. The shape of rectal …