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.

Supervised learning

semantic segmentation

multi-atlas segmentation

conditional random fields

label fusion

feature-based registration

image registration

random decision forests

convolutional neural networks

medical image segmentation

EC, Hörsalsvägen 11, Göteborg
Opponent: Associate Professor Robin Strand (1) Centre for Image Analysis, Division of Visual Information and Interaction, Dept. of Information Technology, Uppsala University (2) Section of Radiology, Dept. of Surgical Sciences, Uppsala University

Author

Jennifer Alvén

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Überatlas: Fast and robust registration for multi-atlas segmentation

Pattern Recognition Letters,; Vol. 80(2016)p. 249-255

Journal article

Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography

Journal of Medical Imaging,; Vol. 3(2016)p. Article number 034003-

Journal article

Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),; Vol. 9127(2015)p. 92-102

Paper in proceedings

Good Features for Reliable Registration in Multi-Atlas Segmentation

Proceedings of the VISCERAL Anatomy3 Segmentation Challenge co-located with IEEE International Symposium on Biomedical Imaging (ISBI 2015),; Vol. 1390(2015)p. 12-17

Paper in proceedings

Shape-aware multi-atlas segmentation

Proceedings - 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 4-8 December 2016,; (2017)p. 1101-1106

Paper in proceedings

Alvén, J., Kahl, F., Landgren, M., Larsson, V., Ulén, J., Enqvist, O. Shape-Aware Label Fusion for Multi-Atlas Frameworks.

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

Publisher

Chalmers University of Technology

EC, Hörsalsvägen 11, Göteborg

Opponent: Associate Professor Robin Strand (1) Centre for Image Analysis, Division of Visual Information and Interaction, Dept. of Information Technology, Uppsala University (2) Section of Radiology, Dept. of Surgical Sciences, Uppsala University

More information

Created

8/14/2017