GAS: A genetic atlas selection strategy in multi-atlas segmentation framework.

Multi-Atlas based Segmentation (MAS) algorithms have been successfully applied to many medical image segmentation tasks, but their success relies on a large number of atlases and good image registration performance. Choosing well-registered atlases for label fusion is vital for an accurate segmentation. This choice becomes even more crucial when the segmentation involves organs characterized by a high anatomical and pathological variability. In this paper, we propose a new genetic atlas selection strategy (GAS) that automatically chooses the best subset of atlases to be used for segmenting the target image, on the basis of both image similarity and segmentation overlap. More precisely, the key idea of GAS is that if two images are similar, the performances of an atlas for segmenting each image are similar. Since the ground truth of each atlas is known, GAS first selects a predefined number of similar images to the target, then, for each one of them, finds a near-optimal subset of atlases by means of a genetic algorithm. All these near-optimal subsets are then combined and used to segment the target image. GAS was tested on single-label and multi-label segmentation problems. In the first case, we considered the segmentation of both the whole prostate and of the left ventricle of the heart from magnetic resonance images. Regarding multi-label problems, the zonal segmentation of the prostate into peripheral and transition zone was considered. The results showed that the performance of MAS algorithms statistically improved when GAS is used.

The role of multi-parametric MRI in loco-regional staging of men diagnosed with early prostate cancer.

PURPOSE OF REVIEW: To review the use of multi-parametric MRI (mpMRI) in loco-regional assessment of men with early prostate cancer. RECENT FINDINGS: mpMRI comprises anatomic T2 and T1 sequences supplemented by functional imaging techniques such as diffusion-weighted and dynamic contrast enhanced (DCE) imaging. mpMRI is gaining increasing acceptance for prostate cancer detection and staging of early disease. It can facilitate targeted therapies, guide surgical options and enable active surveillance within suitable patients. The technique can be performed at 1.5 or 3 Tesla, but sequence optimization is critical to successful implementation of mpMRI. T2 and diffusion-weighted sequences are minimal requirements and are often complemented by DCE images. When performed at high spatial resolution, DCE facilitates detection of disease, as well as assessment of extra-capsular extension, distal urethral sphincter and seminal vesicles involvement. Pre-biopsy mpMRI is recommended for both detection and staging as it avoids biopsy artefact, and when normal, has a negative predictive value of 95% for significant cancer. SUMMARY: mpMRI reliably detects clinically significant prostate tumour and ideally should be performed prior to biopsy. It provides an accurate method for local disease staging and facilitates a growing range of treatment options for patients with early disease.