Automated multi-organ segmentation presents a considerable challenge due to the diversity
of organs and individual variations. Current state-of-the-art deep learning techniques …

Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce
dense voxel-wise predictions of full volumetric images. In this work, we show that a multi …

Multi-organ segmentation is vital for clinical diagnosis and treatment. Although CNN and its
extensions are popular in organ segmentation, they suffer from the local receptive field. In …

Most fully automatic segmentation approaches target a single anatomical structure in a
specific combination of image modalities and are often difficult to extend to other modalities …

Multi-organ segmentation, which identifies and separates different organs in medical
images, is a fundamental task in medical image analysis. Recently, the immense success of …

Multi-organ segmentation is one of most successful applications of deep learning in medical
image analysis. Deep convolutional neural nets (CNNs) have shown great promise in …

Automated multi-organ segmentation is an increasingly important field of research enabling
a holistic assessment of the human body. Deep learning-based methods, which are …

Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …

Accurate delineation of multiple organs is a critical process for various medical procedures,
which could be operator-dependent and time-consuming. Existing organ segmentation …

Performing coarse-to-fine abdominal multi-organ segmentation facilitates extraction of high-
resolution segmentation minimizing the loss of spatial contextual information. However …