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Minimally Interactive Segmentation with Application to Human Placenta in Fetal MR Images

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Minimally Interactive Segmentation with Application to Human Placenta in Fetal MR Images Minimally Interactive Segmentation with Application to Human Placenta in Fetal MR Images Guotai Wang A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy of University College London. Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London June 8, 2018 2 3 I, Guotai Wang, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the work. Abstract Placenta segmentation from fetal Magnetic Resonance (MR) images is important for fetal surgical planning. However, accurate segmentation results are difficult to achieve for automatic methods, due to sparse acquisition, inter-slice motion, and the widely varying position and shape of the placenta among pregnant women. Interactive meth- ods have been widely used to get more accurate and robust results. A good interactive segmentation method should achieve high accuracy, minimize user interactions with low variability among users, and be computationally fast. Exploiting recent advances in machine learning, I explore a family of new interactive methods for placenta seg- mentation from fetal MR images. I investigate the combination of user interactions with learning from a single image or a large set of images. For learning from a single image, I propose novel Online Random Forests to efficiently leverage user interactions for the segmentation of 2D and 3D fetal MR images. I also investigate co-segmentation of multiple volumes of the same patient with 4D Graph Cuts. For learning from a large set of images, I first propose a deep learning-based framework that combines user interactions with Convolutional Neural Networks (CNN) based on geodesic distance transforms to achieve accurate segmentation and good interactivity. I then propose image-specific fine-tuning to make CNNs adaptive to different individual images and able to segment previously unseen objects. Experimental results show that the pro- posed algorithms outperform traditional interactive segmentation methods in terms of accuracy and interactivity. Therefore, they might be suitable for segmentation of the placenta in planning systems for fetal and maternal surgery, and for rapid characteriza- tion of the placenta by MR images. I also demonstrate that they can be applied to the segmentation of other organs from 2D and 3D images. Impact Statement The algorithms and software for image segmentation developed in this thesis have the potential to lead to benefits both inside and outside academia in a variety of ways. First, they will benefit academic disciplines including machine learning and med- ical imaging. The dynamically balanced Online Random Forests developed in this thesis addresses a general problem of learning from imbalanced data with a chang- ing imbalance ratio. It can be applied to not only interactive image segmentation, but also broader learning problems such as data stream classification in financial distress prediction. The proposed deep interactive segmentation methods address the problem of encoding user interactions with convolutional neural networks, which opens a new stream of research in interactive medical image computing with deep learning. These algorithms may also benefit researchers in the field of fetal growth assessment, mod- eling and surgical intervention. In the meanwhile, I contributed to the deep learning software for medical images NiftyNet1, which may help researchers in the medical image computing community. Secondly, researches in this thesis has the potential to benefit clinical practice and healthcare. The segmentation methods developed in this research may assist Radiol- ogists to segment the placenta more efficiently. The new algorithms can potentially help them get accurate segmentation results with far less time and therefore reduce their burden. Surgeons may also benefit from the developed algorithms to obtain the segmentation results for better surgical planning, and clinicians may benefit from them for better diagnosis of the developing fetus. All these aspects have the potential to improve maternal and fetal healthcare. This impact could be brought about through 1http://niftynet.io 8 Impact Statement translational researches, collaborations with clinicians and commercial activities. Thirdly, the research output may benefit industries such as medical imaging com- panies. This may help them develop better software for clinical use and novel products based on intelligent image computing. This impact could be brought about through patents and collaborations with technology transfer companies. Acknowledgements I would like to sincerely thank my primary supervisor Prof. Sebastien´ Ourselin, for his strong support, invaluable guidance and continuous encouragement throughout my PhD. My sincere gratitude also goes to my secondary supervisor Tom Vercauteren and tertiary supervisor Maria A. Zuluaga and Juan Eugenio Iglesias for their very insightful advice and in-depth discussions during my research project. I am also grateful to my clinical supervisor Prof. Jan Deprest for his suggestions and kind support. I would like to thank Anna L. David, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Wenqi Li and Tom Doel for their kind help during my research. I would like to thank as well my lab-mates in Translational Imaging Group, es- pecially Luis, Marcel, Carla, Efthymios, Sebastiano, Michael, Yijing and Jieqing, who made our group an enjoyable working environment. I am also thankful to UCL for funding me with the Overseas Research Scholarship and Graduate Research Scholar- ship. Last but not least, I am profoundly grateful to my family, my father Kaijia Wang, my mother Yuanxiu Shen, and my brother Jinguo Wang, for their extensive and endless support in all the past years. Special thanks from the bottom of my heart go to my wife Yuanyuan Nie, who shared all my pleasure and depression during these years. List of Publications and Patent Applications Peer-reviewed Journal Papers 1. Guotai Wang, Maria A. Zuluaga, Rosalind Pratt, Michael Aertsen, Tom Doel, Maria Klusmann, Anna L. David, Jan Deprest, Tom Vercauteren, and Sebastien´ Ourselin. Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views. Medical Image Anal- ysis, 34:137-147, 2016. 2. Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Maria Klusmann, Anna L. David, Jan Deprest, Sebastien´ Ourselin, and Tom Vercauteren. Interactive segmentation of medical images using deep learning with image-specific fine-tuning. IEEE Transactions on Medical Imaging, In press, 2018. DOI:10.1109/TMI.2018.2791721. 3. Guotai Wang, Maria A. Zuluaga, Wenqi Li, Rosalind Pratt, Premal A. Pa- tel, Michael Aertsen, Tom Doel, Maria Klusmann, Anna L. David, Jan Deprest, Sebastien´ Ourselin, and Tom Vercauteren. DeepIGeoS: A deep interactive geodesic framework for medical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, In press, 2018. DOI:10.1109/TPAMI.2018.2840695. 4. Eli Gibson, Wenqi Li, Carole Sudre, Lucas Fidon, Dzoshkun Shakir, Guotai Wang, Zach Eaton-Rosen, Robert Gray, Tom Doel, Yipeng Hu, Tom Whyntie, 12 List of Publications and Patent Applications Parashkev Nachev, Dean C Barratt, Sbastien Ourselin, M Jorge Cardoso, Tom Vercauteren. NiftyNet: A deep-learning platform for medical imaging. Com- puter Methods and Programs in Biomedicine, 158:113-122, 2018 Peer-reviewed Full-length Conference Papers 1. Guotai Wang, Maria A. Zuluaga, Rosalind Pratt, Michael Aertsen, Anna L. David, Jan Deprest, Tom Vercauteren, and Sebastien´ Ourselin. Slic-Seg: Slice- by-slice segmentation propagation of the placenta in fetal MRI using one-plane scribbles and online learning. In MICCAI, pages 29-37, 2015. 2. Guotai Wang, Maria A. Zuluaga, Rosalind Pratt, Michael Aertsen, Anna L. David, Jan Deprest, Tom Vercauteren, and Sebastien´ Ourselin. Minimally in- teractive placenta segmentation from motion corrupted MRI for fetal surgical planning. In MICCAI Workshop on Interactive Medical Image Computing, 2015 3. Guotai Wang, Maria A. Zuluaga, Rosalind Pratt, Michael Aertsen, Tom Doel, Maria Klusmann, Anna L. David, Jan Deprest, Tom Vercauteren, and Sebastien´ Ourselin. Dynamically balanced online random forests for interactive scribble- based segmentation. In MICCAI, pages 352-360, 2016. 4. Wenqi Li, Guotai Wang, Lucas Fidon, Sebastien´ Ourselin, M. Jorge Cardoso, and Tom Vercauteren. On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task. In IPMI, pages 348-360, 2017. 5. Guotai Wang, Wenqi Li, Sebastien´ Ourselin, and Tom Vercauteren. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural net- works. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pages 178-190, Springer International Publishing, 2018. Patent Applications 1. Guotai Wang,Sebastien´ Ourselin, Tom Vercauteren, Wenqi Li, Lucas Fidon. A system and computer-implemented method for segmenting an image. U.K. List of Publications and Patent Applications 13 Patent GB1709672.8, filed 16 June 2017. Contents 1 Introduction 25 1.1 Placental Anatomy and Abnormality . 26 1.2 Clinical Imaging of the Placenta . 31 1.2.1 Ultrasound . 31 1.2.2 Fetal MRI . 33 1.2.3 Other Modalities . 35 1.3 Segmentation of the Placenta . 36 1.4 Objectives and Challenges . 36 1.5 Thesis Contribution
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