A system for unsupervised extraction of orthopedic parameters from CT data

The request for software assistance is increasingly gaining importance in the field of orthopedic surgery. In the near future more people will need implants, which have to last longer. New developments in computer assisted therapy planning promise to significantly reduce the number of revisions and increase the longevity of an implant. For example the computation of the functional outcome of a total knee replacement by prediction of kinematics may provide important guidance during surgery. Speed, accuracy and as little manual interaction as possible are the key factors to make those new developments available to the clinical routine. To reach this goal we present a software assistant for the reconstruction of individual anatomical models (e.g. geometry and landmarks) from medical image data, which is an essential step in this effort. We will present and discuss present and future application scenarios

A Convex Relaxation for Multi-Graph Matching

We present a convex relaxation for the multi-graph matching problem. Our formulation allows for partial pairwise matchings, guarantees cycle consistency, and our objective incorporates both linear and quadratic costs. Moreover, we also present an extension to higher-order costs. In order to solve the convex relaxation we employ a message passing algorithm that optimizes the dual problem. We experimentally compare our algorithm on established benchmark problems from computer vision, as well as on large problems from biological image analysis, the size of which exceed previously investigated multi-graph matching instances

Fusion moves for graph matching

We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching. Inspired by the success of the fusion moves technique developed for multilabel discrete Markov random fields, we investigate its applicability to graph matching. In particular, we show how fusion moves can be efficiently combined with the dedicated state-of-the-art dual methods that have recently shown superior results in computer vision and bioimaging applications. As our empirical evaluation on a wide variety of graph matching datasets suggests, fusion moves significantly improve performance of these methods in terms of speed and quality of the obtained solutions. Our method sets a new state-of-the-art with a notable margin with respect to its competitors

TLIMB - a transfer learning framework for image analysis of the brain

Biomedical image analysis plays a pivotal role in advancing our understanding of the human body’s functioning across different scales, usually based on deep learning-based methods. However, deep learning methods are notoriously data hungry, which poses a problem in fields where data is difficult to obtain such as in neuroscience. Transfer learning (TL) has become a popular and successful approach to cope with this issue, but is difficult to apply in practise due the many parameters it requires to set properly. Here, we present TLIMB, a novel python-based framework for easy development of optimized and scalable TL-based image analysis pipelines in the neurosciences. TLIMB allows for an intuitive configuration of source / target data sets, specific TL-approach and deep learning-architecture, and hyperparameter optimization method for a given data analysis pipeline and compiles these into a nextflow workflow for seamless execution over different infrastructures, ranging from multicore servers to large compute clusters. Our evaluation using a pipeline for analysing 10.000 MRI images of the human brain from the UK Biobank shows that TLIMB is easy to use, incurs negligible overhead and can scale across different cluster sizes

A comparative study of graph matching algorithms in computer vision

The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. Since a common standard benchmark has not been developed, their performance claims are often hard to verify as evaluation on differing problem instances and criteria make the results incomparable. To address these shortcomings, we present a comparative study of graph matching algorithms. We create a uniform benchmark where we collect and categorize a large set of existing and publicly available computer vision graph matching problems in a common format. At the same time we collect and categorize the most popular open-source implementations of graph matching algorithms. Their performance is evaluated in a way that is in line with the best practices for comparing optimization algorithms. The study is designed to be reproducible and extensible to serve as a valuable resource in the future. Our study provides three notable insights: 1.) popular problem instances are exactly solvable in substantially less than 1 second and, therefore, are insufficient for future empirical evaluations; 2.) the most popular baseline methods are highly inferior to the best available methods; 3.) despite the NP-hardness of the problem, instances coming from vision applications are often solvable in a few seconds even for graphs with more than 500 vertices

3D reconstruction of the human rib cage from 2D projection images using a statistical shape model

Purpose: This paper describes an approach for the three-dimensional (3D) shape and pose reconstruction of the human rib cage from few segmented two-dimensional (2D) projection images. Our work is aimed at supporting temporal subtraction techniques of subsequently acquired radiographs by establishing a method for the assessment of pose differences in sequences of chest radiographs of the same patient. Methods: The reconstruction method is based on a 3D statistical shape model (SSM) of the rib cage, which is adapted to binary 2D projection images of an individual rib cage. To drive the adaptation we minimize a distance measure that quantifies the dissimilarities between 2D projections of the 3D SSM and the projection images of the individual rib cage. We propose different silhouette-based distance measures and evaluate their suitability for the adaptation of the SSM to the projection images. Results: An evaluation was performed on 29 sets of biplanar binary images (posterior-anterior and lateral). Depending on the chosen distance measure, our experiments on the combined reconstruction of shape and pose of the rib cages yield reconstruction errors from 2.2 to 4.7 mm average mean 3D surface distance. Given a geometry of an individual rib cage, the rotational errors for the pose reconstruction range from 0.1 degrees to 0.9 degrees. Conclusion: The results show that our method is suitable for the estimation of pose differences of the human rib cage in binary projection images. Thus, it is able to provide crucial 3D information for registration during the generation of 2D subtraction images

Comparison and evaluation of methods for liver segmentation from CT datasets

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques