Image segmentation is an important step in medical image processing and has been widely
studied and developed for refinement of clinical analysis and applications. New models …

Scoliosis is a common spinal condition where the spine curves to the side and thus deforms
the spine. Curvature estimation provides a powerful index to evaluate the deformation …

Medical image segmentation is a difficult but important task for many clinical operations such
as cardiac bi-ventricular volume estimation. More recently, there has been a shift to utilizing …

Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …

The high prevalence of spinal stenosis results in a large volume of MRI imaging, yet
interpretation can be time-consuming with high inter-reader variability even among the most …

Medical image segmentation is the process of anatomically isolating organs for analysis and
treatment. Leading works within this domain emerged with the well-known U-Net. Despite its …

Deep convolutional networks recently made many breakthroughs in medical image
segmentation. Still, some anatomical artefacts may be observed in the segmentation results …

Scoliosis is a medical condition where a person's spine has a sideways curve. The Cobb
angle quantifying the degree of spinal curvature is the gold standard for a scoliosis …

Multitask learning (MTL) has achieved notable progress in many medical applications. In
this article, we propose a multitask neural network, MRNet, for segmentation and spinal …

Defining the loss function is an important part of neural network design and critically
determines the success of deep learning modeling. A significant shortcoming of the …