Multi-Object Geodesic Active Contours (MOGAC): A Parallel Sparse-Field Algorithm for Image Segmentation

An important task for computer vision systems is to segment adjacent structures in images without producing gaps or overlaps. Multi-object Level Set Methods (MLSM) perform this task with the benefit of sub-pixel accuracy. However, current implementations of MLSM are not as computationally or memory efficient as their region growing and graph cut counterparts which lack sub-pixel accuracy. To address this performance gap, we present a novel parallel implementation of MLSM that leverages the sparse properties of the segmentation algorithm to minimize its memory footprint for multiple objects. The new method, Multi-Object Geodesic Active Contours (MOGAC), can represent N objects with just two functions: a label image and unsigned distance field. The time complexity of the algorithm is shown to be O((M^d)/P) for M^d pixels and P processing units in dimension d={2,3}, independent of the number of objects. Results are presented for 2D and 3D image segmentation problems

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

A multi-compartment segmentation framework with homeomorphic level sets

The simultaneous segmentation of multiple objects is an important problem in many imaging and computer vision applications. Various extensions of level set segmentation techniques to multiple objects have been proposed; however, no one method maintains object relationships, preserves topology, is computationally efficient, and provides an object-dependent internal and external force capability. In this paper, a framework for segmenting multiple objects that permits different forces to be applied to different boundaries while maintaining object topology and relationships is presented. Because of this framework, the segmentation of multiple objects each with multiple compartments is supported, and no overlaps or vacuums are generated. The computational complexity of this approach is independent of the number of objects to segment, thereby permitting the simultaneous segmentation of a large number of components. The properties of this approach and comparisons to existing methods are shown using a variety of images, both synthetic and real. ments within them — a multi-object segmentation problem that requires a preservation of both inclusion and geometric relationships between the objects and compartments. Particularly in the segmentation of normal anatomy in biomedical imaging, the above properties are highly desirable. The extension of GDMs to the segmentation of multiple objects and multi-compartment objects is not straightforward. Difficulties arise because the different forces (internal and/or external) for each object must remain coherent to avoid overlap and vacuum between segmented regions. Similarly, maintaining the topology and relationships during segmentation is a complex and computationally intensive task. Fig. 1 illustrates a simple geometry that actually represents a significant challenge. The three ellipses are to be found in such a way that they remain connected, do not overlap, their outer boundaries conform to the gray level edges, and their inner boundaries are smooth. This is a typical challenge in these multi-compartment problems, and there is no existing method in level set literature solving them. 1