Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …

Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-
the-art results in semantic segmentation for numerous medical imaging applications …

Background and Purpose: The ISLES challenge (Ischemic Stroke Lesion Segmentation)
enables globally diverse teams to compete to develop advanced tools for stroke lesion …

Most image segmentation algorithms are trained on binary masks formulated as a
classification task per pixel. However, in applications such as medical imaging, this “black …

Loss functions, which govern a deep learning-based optimization process, have been
widely investigated to handle the class imbalanced data issue in crack segmentation …

The Dice similarity coefficient (DSC) is both a widely used metric and loss function for
biomedical image segmentation due to its robustness to class imbalance. However, it is well …

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord
cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or …

Recent research on COVID-19 suggests that CT imaging provides useful information to
assess disease progression and assist diagnosis, in addition to help understanding the …

Neural network-based segmentation methods are an important advancement in medical
image analysis. Issues with class imbalance pose a significant challenge in medical …