Improving Multi-Atlas Segmentation Methods for Medical Images

Licentiate thesis, 2017

Semantic segmentation of organs or tissues, i.e. delineating anatomically or physiologically meaningful boundaries, is an essential task in medical image analysis. One particular class of automatic segmentation algorithms has proved to excel at a diverse set of medical applications, namely multi-atlas segmentation. However, these multi-atlas methods exhibit several issues recognized in the literature. Firstly, multi-atlas segmentation requires several computationally expensive image registrations. In addition, the registration procedure needs to be executed with a high accuracy in order to enable competitive segmentation results. Secondly, up-to-date multi-atlas frameworks require large sets of labelled data to model all possible anatomical variations. Unfortunately, acquisition of manually annotated medical data is time-consuming which needless to say limits the applicability. Finally, standard multi-atlas approaches pose no explicit constraints on the output shape and thus allow for implausibly segmented anatomies. This thesis includes four papers addressing the difficulties associated with multi-atlas segmentation in several ways; by speeding up and increasing the accuracy of feature-based registration methods, by incorporating explicit shape models into the label fusion framework using robust optimization techniques and by refining the solutions with means of machine learning algorithms, such as random decision forests and convolutional neural networks, taking both performance and data-efficiency into account. The proposed improvements are evaluated on three medical segmentation tasks with vastly different characteristics; pericardium segmentation in cardiac CTA images, region parcellation in brain MRI and multi-organ segmentation in whole-body CT images. Extensive experimental comparisons to previously published methods show promising results on par or better than state-of-the-art as of date.