Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients

TL;DR: A survey of published MAS algorithms and studies that have applied these methods to various biomedical problems and a perspective on the future of MAS, which, it is believed, will be one of the dominant approaches in biomedical image segmentation.

TL;DR: Currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment, and the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.

TL;DR: Traditional (nondeep learning) algorithms particularly relevant for applications in radiotherapy are reviewed and concepts from deep learning are introduced focusing on convolutional neural networks and fully-convolutional networks which are generally used for segmentation tasks.

TL;DR: An end-to-end deep deconvolutional neural network (DDNN) has the potential to improve the consistency of contouring and streamline radiotherapy workflows, but careful human review and a considerable amount of editing will be required.

TL;DR: The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.

TL;DR: A practicable procedure is demonstrated that exceeds the accuracy of previous automatic methods and can compete with manual delineations in segmentation propagation and decision fusion of fused brain segmentations.

TL;DR: The findings show that atlas selection is an important issue in atlas-based segmentation and that, in particular, multi-classifier techniques can substantially increase the segmentation accuracy.

TL;DR: It is demonstrated that, in comparison with manual segmentation, atlas-based automatic LNS for head-and-neck cancer is accurate, efficient, and reduces interobserver variability.