PurposeTo improve multi‐atlas segmentation of the skeleton from whole‐body MRI. In particular, we study the effect of employing the atlas segmentations to iteratively mask tissues outside of the region of interest to improve the atlas alignment and subsequent segmentation.MethodsAn improved atlas registration scheme is proposed. Starting from a suitable initial alignment, the alignment is refined by introducing additional stages of deformable registration during which the image sampling is limited to the dilated atlas segmentation label mask. The performance of the method was demonstrated using leave‐one‐out cross‐validation using atlases of 10 whole‐body 3D‐T1 images of prostate cancer patients with bone metastases and healthy male volunteers, and compared to existing state of the art. Both registration accuracy and resulting segmentation quality, using four commonly used label fusion strategies, were evaluated.ResultsThe proposed method showed significant improvement in registration and segmentation accuracy with respect to the state of the art for all validation criteria and label fusion strategies, resulting in a Dice coefficient of 0.887 (STEPS label fusion). The average Dice coefficient for the multi‐atlas segmentation showed over 11% improvement with a decrease of false positive rate from 28.3% to 13.2%. For this application, repeated application of the background masking did not lead to significant improvement of the segmentation result.ConclusionsA registration strategy, relying on the use of atlas segmentations as mask during image registration was proposed and evaluated for multi‐atlas segmentation of whole‐body MRI. The approach significantly improved registration and final segmentation accuracy and may be applicable to other structures of interest.

The value of whole-body MRI is constantly growing and is currently employed in several bone pathologies including diagnosis and prognosis of multiple myeloma, musculoskeletal imaging and evaluation of treatment response assessment in bone metastases. Intra-patient follow-up MR images acquired over time do not only suffer from spatial misalignments caused by change in patient positioning and body composition, but also intensity inhomogeneities, making the absolute MR intensity values inherently non-comparable. The non-quantitative nature of whole-body MRI makes it difficult to derive reproducible measurement and limits the use of treatment response maps. In this work, we have investigated and compared the performance of several standardization algorithms for skeletal tissue in anatomical and diffusion-weighted whole-body MRI. The investigated method consists of two steps. First, the follow-up whole-body image is spatially registered to a baseline image using B-spline deformable registration. Secondly, an intensity standardization algorithm based on a histogram matching is applied to the followup image. Additionally, the use of a skeleton mask was introduced, in order to focus the accuracy of algorithms on a tissue of interest. A linear piecewise matching method using masked skeletal region showed a superior performance in comparison to the other evaluated intensity standardization methods. The proposed work helps to overcome the nonquantitative nature of whole-body MRI images, allowing for extraction of important image parameters, visualization of whole-body MR treatment response maps and assessment of severity of bone pathology based on MR intensity profile.