3D shape instantiation for intra-operative navigation from a single 2D projection

Unlike traditional open surgery where surgeons can see the operation area clearly, in robot-assisted Minimally Invasive Surgery (MIS), a surgeon’s view of the region of interest is usually limited. Currently, 2D images from fluoroscopy, Magnetic Resonance Imaging (MRI), endoscopy or ultrasound are used for intra-operative guidance as real-time 3D volumetric acquisition is not always possible due to the acquisition speed or exposure constraints. 3D reconstruction, however, is key to navigation in complex in vivo geometries and can help resolve this issue. Novel 3D shape instantiation schemes are developed in this thesis, which can reconstruct the high-resolution 3D shape of a target from limited 2D views, especially a single 2D projection or slice. To achieve a complete and automatic 3D shape instantiation pipeline, segmentation schemes based on deep learning are also investigated. These include normalization schemes for training U-Nets and network architecture design of Atrous Convolutional Neural Networks (ACNNs). For U-Net normalization, four popular normalization methods are reviewed, then Instance-Layer Normalization (ILN) is proposed. It uses a sigmoid function to linearly weight the feature map after instance normalization and layer normalization, and cascades group normalization after the weighted feature map. Detailed validation results potentially demonstrate the practical advantages of the proposed ILN for effective and robust segmentation of different anatomies. For network architecture design in training Deep Convolutional Neural Networks (DCNNs), the newly proposed ACNN is compared to traditional U-Net where max-pooling and deconvolutional layers are essential. Only convolutional layers are used in the proposed ACNN with different atrous rates and it has been shown that the method is able to provide a fully-covered receptive field with a minimum number of atrous convolutional layers. ACNN enhances the robustness and generalizability of the analysis scheme by cascading multiple atrous blocks. Validation results have shown the proposed method achieves comparable results to the U-Net in terms of medical image segmentation, whilst reducing the trainable parameters, thus improving the convergence and real-time instantiation speed. For 3D shape instantiation of soft and deforming organs during MIS, Sparse Principle Component Analysis (SPCA) has been used to analyse a 3D Statistical Shape Model (SSM) and to determine the most informative scan plane. Synchronized 2D images are then scanned at the most informative scan plane and are expressed in a 2D SSM. Kernel Partial Least Square Regression (KPLSR) has been applied to learn the relationship between the 2D and 3D SSM. It has been shown that the KPLSR-learned model developed in this thesis is able to predict the intra-operative 3D target shape from a single 2D projection or slice, thus permitting real-time 3D navigation. Validation results have shown the intrinsic accuracy achieved and the potential clinical value of the technique. The proposed 3D shape instantiation scheme is further applied to intra-operative stent graft deployment for the robot-assisted treatment of aortic aneurysms. Mathematical modelling is first used to simulate the stent graft characteristics. This is then followed by the Robust Perspective-n-Point (RPnP) method to instantiate the 3D pose of fiducial markers of the graft. Here, Equally-weighted Focal U-Net is proposed with a cross-entropy and an additional focal loss function. Detailed validation has been performed on patient-specific stent grafts with an accuracy between 1-3mm. Finally, the relative merits and potential pitfalls of all the methods developed in this thesis are discussed, followed by potential future research directions and additional challenges that need to be tackled.Open Acces

Blurry Boundary Delineation and Adversarial Confidence Learning for Medical Image Analysis

Low tissue contrast and fuzzy boundaries are major challenges in medical image segmentation which is a key step for various medical image analysis tasks. In particular, blurry boundary delineation is one of the most challenging problems due to low-contrast and even vanishing boundaries. Currently, encoder-decoder networks are widely adopted for medical image segmentation. With the lateral skip connection, the models can obtain and fuse both semantic and resolution information in deep layers to achieve more accurate segmentation performance. However, in many applications (e.g., images with blurry boundaries), these models often cannot precisely locate complex boundaries and segment tiny isolated parts. To solve this challenging problem, we empirically analyze why simple lateral connections in encoder-decoder architectures are not able to accurately locate indistinct boundaries. Based on the analysis, we argue learning high-resolution semantic information in the lateral connection can better delineate the blurry boundaries. Two methods have been proposed to achieve such a goal. a) A high-resolution pathway composed of dilated residual blocks has been adopted to replace the simple lateral connection for learning the high-resolution semantic features. b) A semantic-guided encoder feature learning strategy is further proposed to learn high-resolution semantic encoder features so that we can more accurately and efficiently locate the blurry boundaries. Besides, we also explore a contour constraint mechanism to model blurry boundary detection. Experimental results on real clinical datasets (infant brain MRI and pelvic organ datasets) show that our proposed methods can achieve state-of-the-art segmentation accuracy, especially for the blurry regions. Further analysis also indicates that our proposed network components indeed contribute to the performance gain. Experiments on an extra dataset also validate the generalization ability of our proposed methods. Generative adversarial networks (GANs) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis. In these works, adversarial learning is usually directly applied to the original supervised segmentation (synthesis) networks. The use of adversarial learning is effective in improving visual perception performance since adversarial learning works as realistic regularization for supervised generators. However, the quantitative performance often cannot be improved as much as the qualitative performance, and it can even become worse in some cases. In this dissertation, I explore how adversarial learning could be more useful in supervised segmentation (synthesis) models, i.e., how to synchronously improve visual and quantitative performance. I first analyze the roles of discriminator in the classic GANs and compare them with those in supervised adversarial systems. Based on this analysis, an adversarial confidence learning framework is proposed for taking better advantage of adversarial learning; that is, besides the adversarial learning for emphasizing visual perception, the confidence information provided by the adversarial network is utilized to enhance the design of the supervised segmentation (synthesis) network. In particular, I propose using a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation (synthesis) network. Furthermore, various loss functions of GANs are investigated and the binary cross entropy loss is finally chosen to train the proposed adversarial confidence learning system so that the modeling capacity of the discriminator is retained for confidence learning. With these settings, two machine learning algorithms are proposed to solve some specific medical image analysis problems. a) A difficulty-aware attention mechanism is proposed to properly handle hard samples or regions by taking structural information into consideration so that the irregular distribution of medical data could be appropriately dealt with. Experimental results on clinical and challenge datasets show that the proposed algorithm can achieve state-of-the-art segmentation (synthesis) accuracy. Further analysis also indicates that adversarial confidence learning can synchronously improve the visual perception and quantitative performance. b) A semisupervised segmentation model is proposed to alleviate the everlasting challenge for medical image segmentation - lack of annotated data. The proposed method can automatically recognize well-segmented regions (instead of the entire sample) and dynamically include them to increase the label set during training. Specifically, based on the confidence map, a region-attention based semi-supervised learning strategy is designed to further train the segmentation network. Experimental results on real clinical datasets show that the proposed approach can achieve better segmentation performance with extra unannotated data.Doctor of Philosoph

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd