Deformable reconstruction of histology sections using structural probability maps.

The reconstruction of a 3D volume from a stack of 2D histology slices is still a challenging problem especially if no external references are available. Without a reference, standard registration approaches tend to align structures that should not be perfectly aligned. In this work we introduce a deformable, reference-free reconstruction method that uses an internal structural probability map (SPM) to regularize a free-form deformation. The SPM gives an estimate of the original 3D structure of the sample from the misaligned and possibly corrupted 2D slices. We present a consecutive as well as a simultaneous reconstruction approach that incorporates this estimate in a deformable registration framework. Experiments on synthetic and mouse brain datasets indicate that our method produces similar results compared to reference-based techniques on synthetic datasets. Moreover, it improves the smoothness of the reconstruction compared to standard registration techniques on real data.

Interventional tool tracking using discrete optimization.

This work presents a novel scheme for tracking of motion and deformation of interventional tools such as guide-wires and catheters in fluoroscopic X-ray sequences. Being able to track and thus to estimate the correct positions of these tools is crucial in order to offer guidance enhancement during interventions. The task of estimating the apparent motion is particularly challenging due to the low signal-to-noise ratio (SNR) of fluoroscopic images and due to combined motion components originating from patient breathing and tool interactions performed by the physician. The presented approach is based on modeling interventional tools with B-splines whose optimal configuration of control points is determined through efficient discrete optimization. Each control point corresponds to a discrete random variable in a Markov random field (MRF) formulation where a set of labels represents the deformation space. In this context, the optimal curve corresponds to the maximum a posteriori (MAP) estimate of the MRF energy. The main motivation for employing a discrete approach is the possibility to incorporate a multi-directional search space which is robust to local minima. This is of particular interest for curve tracking under large deformation. This work analyzes feasibility of employing efficient first-order MRFs for tracking. In particular it shows how to achieve a good compromise between energy approximations and computational efficiency. Experimental results suggest to define both the external and internal energy in terms of pairwise potential functions. The method was successfully applied to the tracking of guide-wires in fluoroscopic X-ray sequences of several hundred frames which requires extremely robust techniques. Comparisons with state-of-the-art guide-wire tracking algorithms confirm the effectiveness of the proposed method.

Reconstruction of 3-D histology images by simultaneous deformable registration.

The reconstruction of histology sections into a 3-D volume receives increased attention due to its various applications in modern medical image analysis. To guarantee a geometrically coherent reconstruction, we propose a new way to register histological sections simuItaneously to previously acquired reference images and to neighboring slices in the stack. To this end, we formulate two potential functions and associate them to the same Markov random field through which we can efficiently find an optimal solution. Due to our simultaneous formulation and the absence of any segmentation step during the reconstruction we can dramatically reduce error propagation effects. This is illustrated by experiments on carefully created synthetic as well as real data sets.

A machine learning approach for deformable guide-wire tracking in fluoroscopic sequences.

Deformable guide-wire tracking in fluoroscopic sequences is a challenging task due to the low signal to noise ratio of the images and the apparent complex motion of the object of interest. Common tracking methods are based on data terms that do not differentiate well between medical tools and anatomic background such as ribs and vertebrae. A data term learned directly from fluoroscopic sequences would be more adapted to the image characteristics and could help to improve tracking. In this work, our contribution is to learn the relationship between features extracted from the original image and the tracking error. By randomly deforming a guide-wire model around its ground truth position in one single reference frame, we explore the space spanned by these features. Therefore, a guide-wire motion distribution model is learned to reduce the intrisic dimensionality of this feature space. Random deformations and the corresponding features can be then automatically generated. In a regression approach, the function mapping this space to the tracking error is learned. The resulting data term is integrated into a tracking framework based on a second-order MAP-MRF formulation which is optimized by QPBO moves yielding high-quality tracking results. Experiments conducted on two fluoroscopic sequences show that our approach is a promising alternative for deformable tracking of guide-wires.