Introduction – Diagnosis of abnormal cilia function is based on ultrastructural analysis of axoneme defects, especialy the features of inner and outer dynein arms which are the motors of ciliar motility. Sub-optimal biopsy material, methodical, and intrinsic electron microscopy factors pose difficulty in ciliary defects evaluation. We present a computer-assisted approach based on state-of-the-art image analysis and object recognition methods yielding a time-saving and efficient diagnosis of cilia dysfunction. Method – The presented approach is based on a pipeline of basal image processing methods like smoothing, thresholding and ellipse fitting. However, integration of application specific knowledge results in robust segmentations even in cases of image artifacts. The method is build hierarchically starting with the detection of cilia within the image, followed by the detection of nine doublets within each analyzable cilium, and ending with the detection of dynein arms of each doublet. The process is concluded by a rough classification of the dynein arms as basis for a computer-assisted diagnosis. Additionally, the interaction possibilities are designed in a way, that the results are still reproducible given the completion report. Results – A qualitative evaluation showed reasonable detection results for cilia, doublets and dynein arms. However, since a ground truth is missing, the variation of the computer-assisted diagnosis should be within the subjective bias of human diagnosticians. The results of a first quantitative evaluation with five human experts and six images with 12 analyzable cilia showed, that with default parameterization 91.6% of the cilia and 98% of the doublets were found. The computer-assisted approach rated 66% of those inner and outer dynein arms correct, where all human experts agree. However, especially the quality of the dynein arm classification may be improved in future work.
Introduction - The segmentation of rat brain slices suffers from illumination inhomogeneities and staining effects.
State-of-the-art level-set methods model slice and background with intensity mixture densities defining the
speed function as difference between the respective probabilites. Nevertheless, the overlap of these distributions
causes an inaccurate stopping at the slice border. In this work, we propose the characterisation of the border
area with intensity pairs for inside and outside estimating joint intensity probabilities.
Method - In contrast to global object and background models, we focus on the object border characterised by
a joint mixture density. This specifies the probability of the occurance of an inside and an outside value in direct
adjacency. These values are not known beforehand, because inside and outside depend on the level-set evolution
and change during time. Therefore, the speed function is computed time-dependently at the position of the
current zero level-set. Along this zero level-set curve, the inside and outside values are derived as mean along
the curvature normal directing inside and outside the object. Advantage of the joint probability distribution is
to resolve the distribution overlaps, because these are assumed to be not located at the same border position.
Results - The novel time-dependent joint probability based speed function is compared expermimentally with
single probability based speed functions. Two rat brains with about 40 slices are segmented and the results
analysed using manual segmentations and the Tanimoto overlap measure. Improved results are recognised for
both data sets.
Introduction - Fusion of histology and MRI is frequently demanded in biomedical research to study in vitro
tissue properties in an in vivo reference space. Distortions and artifacts caused by cutting and staining of
histological slices as well as differences in spatial resolution make even the rigid fusion a difficult task. State-of-
the-art methods start with a mono-modal restacking yielding a histological pseudo-3D volume. The 3D
information of the MRI reference is considered subsequently. However, consistency of the histology volume and
consistency due to the corresponding MRI seem to be diametral goals. Therefore, we propose a novel fusion
framework optimizing histology/histology and histology/MRI consistency at the same time finding a balance
between both goals.
Method - Direct slice-to-slice correspondence even in irregularly-spaced cutting sequences is achieved by
registration-based interpolation of the MRI. Introducing a weighted multi-image mutual information metric
(WI), adjacent histology and corresponding MRI are taken into account at the same time. Therefore, the
reconstruction of the histological volume as well as the fusion with the MRI is done in a single step.
Results - Based on two data sets with more than 110 single registrations in all, the results are evaluated
quantitatively based on Tanimoto overlap measures and qualitatively showing the fused volumes. In comparison
to other multi-image metrics, the reconstruction based on WI is significantly improved. We evaluated different
parameter settings with emphasis on the weighting term steering the balance between intra- and inter-modality