Spatial Structure-Aware Hybrid Segmentation For Medical Image Analysis

Since last decade,convolutional neural networks(CNN)have shown significantly improved results with great potential in medical imaging applications for assisting medical experts to understand the complex anatomy of affected organs for surgical plans.Even though CNN provides satisfactory improvements for image segmentation,still existing methods often lack to provide features like accurate searching,localization,and segmentation of the targeted affected object in a real-time environment.Moreover,existing approaches based on CNN have not specifically designed in a hybrid way to provide improved features to medical experts.Based on the recent technological requirement,this study proposed three different methods in the context of developing hybrid systems to deliver spatial structure-aware hybrid segmentation along with better visualization.Our work tackles the segmentation from three different aspects in term of data type and nature of to be proposed hybrid system.In the context of developing hybrid system,first we have proposed spatial pyramid based searching and tagging of liver’s intraoperative views using convolution neural network(SPST-CNN).By exploiting a hybrid combination of an image pyramid at input and spatial pyramid pooling layer at deeper stages of SPST-CNN,we reveal the gains of full-image representations for accurate searching and tagging variable scaled liver live views.Laparoscopic liver surgery is challenging to perform because of compromised ability of the surgeon to localize subsurface anatomy due to minimal invasive visibility.Our proposed SPST-CNN provides pinpoint searching and tagging of intraoperative liver views to obtain up-to-date information about the location and shape of the area of interest.Downsampling input using an image pyramid enables SPST-CNN framework to deploy input images with a diversity of resolutions for achieving scale invariance feature.As the main objective of thesis was to provide system for spatial structure-aware hybrid segmentation by employing modifications in neural networks,we further explored the topic in hand based on the neural networks methodologies for efficient and precise hybrid systems.We propose an Optimally Fused Fully end-to-end Network(OFF-e NET)for automatic segmentation of the volumetric 3D intracranial vascular structures.Intracranial blood vessels segmentation from computed tomography angiography(CTA)volumes is a promising biomarker for diagnosis and therapeutic treatment in cerebrovascular diseases.The state-of-the-art in medical image segmentation methods is reliant on deep learning architectures based on convolutional neural networks.However,despite their popularity,there is a research gap in the current deep learning architectures optimized to address the technical challenges in blood vessel segmentation.These challenges include:(i)the extraction of concrete brain vessels close to the skull;and(ii)the precise marking of the vessel locations.The proposed hybrid system named OFF-e NET comprises of three modules.In the first module,we exploit the up-skip connections to enhance information flow and dilated convolution for detailed preservation of spatial features maps that is designed for thin blood vessels.In the second module,we employ residual mapping along with inception module for speedy network convergence and richer visual representation.For the third module,we make use of the transferred knowledge in the form of cascaded training strategy to gradually optimize the three segmentation stages(basic,complete,and enhanced)to segment thin vessels located close to the skull.All these modules are designed to be computationally efficient.Our OFF-e NET evaluated using 70 CTA volumes,resulted in 90.75% performance in the segmentation of intracranial blood vessels and outperformed the state-of-the-art counterparts.Moreover,for delivering spatial structure-aware hybrid segmentation feature we have proposed a hybrid system to visually analyze the anatomical variants of the liver vessels anatomy to maximize similarity for finding a suitable Living Donor Recipient(LDR)pair.Liver vessels are segmented from computed tomography angiography(CTA)volumes by employing a cascade incremental learning(CIL)model.Our CIL architecture is able to find optimal solutions,which we use to update the model with liver vessel CTA images.A novel ternary tree-based algorithm is proposed to map all the possible liver vessel variants into their respective tree topologies.The tree topologies of the recipient’s and donor’s liver vessels are then used for an appropriate matching.The proposed algorithm utilizes a set of defined vessel tree variants,which are updated to maintain the maximum matching options by leveraging the accurate segmentation results of the vessels derived from the incremental learning ability of the CIL.We introduce a novel concept of in-order digital string-based comparison to match the geometry of two anatomically varied trees.Experiments through visual illustrations and quantitative analysis demonstrated the effectiveness of our approach compared to the state-of-the-art.An inclusive experimental analysis of our hybrid solutions reveals the capability of delivering enhanced segmentation by providing better visualization capabilities as a cost effective solution than the conventional neural network-based model.