Probabilistic Tracking and Model-Based Segmentation of 3D Tubular Structures. In: Meinzer H, Deserno TM, Handels H, Tolxdorff T, editors. Bildverarbeitung für die Medizin 2009. Berlin: Springer Berlin Heidelberg; 2009. pp. 41-5. [DOI: 10.1007/978-3-540-93860-6_9]

DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes

We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel trees and networks and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D networks, high-class imbalance arising from the low percentage (<3%) of vessel voxels, and unavailability of accurately annotated 3-D training data-and offer solutions as the building blocks of DeepVesselNet. First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context information at a reduced computational burden. Second, we introduce a class balancing cross-entropy loss function with false-positive rate correction to handle the high-class imbalance and high false positive rate problems associated with existing loss functions. Finally, we generate a synthetic dataset using a computational angiogenesis model capable of simulating vascular tree growth under physiological constraints on local network structure and topology and use these data for transfer learning. We demonstrate the performance on a range of angiographic volumes at different spatial scales including clinical MRA data of the human brain, as well as CTA microscopy scans of the rat brain. Our results show that cross-hair filters achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy that does not differ from full 3-D filters. Our class balancing metric is crucial for training the network, and transfer learning with synthetic data is an efficient, robust, and very generalizable approach leading to a network that excels in a variety of angiography segmentation tasks. We observe that sub-sampling and max pooling layers may lead to a drop in performance in tasks that involve voxel-sized structures. To this end, the DeepVesselNet architecture does not use any form of sub-sampling layer and works well for vessel segmentation, centerline prediction, and bifurcation detection. We make our synthetic training data publicly available, fostering future research, and serving as one of the first public datasets for brain vessel tree segmentation and analysis.

Imaging, Analysing and Interpreting Branching Morphogenesis in the Developing Kidney

The kidney develops as an outgrowth of the epithelial nephric duct known as the ureteric bud, in a position specified by a range of rostral and caudal factors which serve to ensure two kidneys form in the appropriate positions in the body. At its simplest level, kidney development can be viewed as the process by which this single bud then undergoes a process of arborisation to form a complex connected network of ducts which will serve to drain urine from the nephrons in the adult organ. The process of bud elaboration is dictated by factors expressed by both the bud itself and by surrounding cells of the metanephric mesenchyme which control cell division and bifurcation. These cells play two critical roles. Firstly, they potentiate the ongoing elaboration of the ureteric tree: remove them and branching ceases. Secondly, they harbour progenitor cells which are fated to undergo their own process of tubulogenesis to form the nephrons of the adult organ. In this chapter, we will discuss how the ureteric bud arises in the developing embryo, how it undergoes branching, how we can measure and study this process and finally the likely relevance that this process has for our understanding of congenital and acquired kidney disease.