Deep neural networks have been tremendously successful at segmenting objects in images.
However, it has been shown they still have limitations on challenging problems such as the …

We propose a shape prior representation-constrained multi-scale features fusion
segmentation network for medical image segmentation, including training and testing …

Deep convolutional neural networks (CNNs) have been widely used for medical image
segmentation. In most studies, only the output layer is exploited to compute the final …

Deep learning models represent the state of the art in medical image segmentation. Most of
these models are fully-convolutional networks (FCNs), namely each layer processes the …

Segmentation is a key step for various medical image analysis tasks. Recently, deep neural
networks could provide promising solutions for automatic image segmentation. The network …

The encoder-decoder model is a commonly used Deep learning (DL) model for medical
image segmentation. Encoder-decoder models make pixel-wise predictions that focus …

Medical image segmentation is a fundamental task for medical image analysis and surgical
planning. In recent years, UNet-based networks have prevailed in the field of medical image …

Pre‐segmentation is known as a crucial step in medical image analysis. Many approaches
have been proposed to make improvement to both the quality and efficiency of …

The accuracy of diagnosis in medical systems requires automatic image segmentation
techniques to provide accurate segmented images of lesions. Segmented images need to …

Besides network architecture, researchers have recently focused their attention on the loss
function for the Convolutional Neural Network-based medical image segmentation. The loss …