Anthropometry of the head and face

A Systematic Nomenclature for the Insect Brain

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

The Liver Tumor Segmentation Benchmark (LiTS)

Honeybees contradict the notion that insect behaviour tends to be relatively inflexible and stereotypical. Indeed, they live in colonies and exhibit complex social, navigational and communication behaviours, as well as a relatively rich cognitive repertoire. Because these relatively complex behaviours are controlled by a brain consisting of only 1 million or so neurons, honeybees offer an opportunity to study the relationship between behaviour and cognition in neural networks that are limited in size and complexity. Most recently, the honeybee has been used to model learning and memory formation, highlighting its utility for neuroscience research, in particular for understanding the basis of cognition.

The honeybee as a model for understanding the basis of cognition.

The honeybee Apis mellifera has emerged as a robust and influential model for the study of classical conditioning, thanks to the existence of a powerful Pavlovian conditioning protocol, the olfactory conditioning of the proboscis extension response (PER) In 2011, the olfactory PER conditioning protocol celebrates 50 years since it was first introduced by Kimihisa Takeda in 1961 Here, we review its origins, developments, and perspectives in order to define future research avenues and necessary methodological and conceptual evolutions We show that olfactory PER conditioning has become a versatile tool for the study of questions in extremely diverse fields in addition to the study of learning and memory and that it has allowed behavioral characterizations, not only of honeybees, but also of other insect species, for which the protocol was adapted We celebrate, therefore, Takeda’s original work and prompt colleagues to conceive and establish further robust behavioral tools for an accurate characterization of insect learning and memory at multiple levels of analysis

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Invertebrate learning and memory: Fifty years of olfactory conditioning of the proboscis extension response in honeybees

Thispaperwill presentourwork on thesimulationof soft tissuedeformationwithin thecontext of maxillofacialsurgery. Our approachis basedon finite elementmethodson tetrahedral grids.Thesegridsrepresent differenttissueregionsandareautomaticallygeneratedwith guaranteedtopologicalcorrectnessandscalableresolutionfrom segmented3D tomographicdata. We describethecompletepipelinefrom creating3D modelsof thepatients’anatomy, thesurgical planning,and the numericalsimulationof soft tissuedeformation. As a result of our investigationwe will assessour approachwith a view to providing a completeplanningand simulationsystemfor clinical use.

Finite-Element Simulation of Soft Tissue Deformation

The exact localization of the mandibular nerve with respect to the bone is important for applications in dental implantology and maxillofacial surgery. Cone beam computed tomography (CBCT), often also called digital volume tomography (DVT), is increasingly utilized in maxillofacial or dental imaging. Compared to conventional CT, however, soft tissue discrimination is worse due to a reduced dose. Thus, small structures like the alveolar nerves are even harder recognizable within the image data. We show that it is nonetheless possible to accurately reconstruct the 3D bone surface and the course of the nerve in a fully automatic fashion, with a method that is based on a combined statistical shape model of the nerve and the bone and a Dijkstra-based optimization procedure. Our method has been validated on 106 clinical datasets: the average reconstruction error for the bone is 0.5±0.1 mm, and the nerve can be detected with an average error of 1.0±0.6 mm.

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Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data

A modelling approach for the realistic simulation of facial expressions of emotion in craniofacial surgery planning is presented. The method is different from conventional, non-physical techniques for character animation in computer graphics. A consistent physiological mechanism for facial expressions was assumed, which was the effect of contracting muscles on soft tissues. For the numerical solution of the linear elastic boundary values, the finite element method on tetrahedral grids was used. The approach was validated on a geometrical model of a human head derived from tomographic data. Using this model, individual facial expressions of emotion were estimated by the superpositioning of precomputed single muscle actions.

Anatomy- and physics-based facial animation for craniofacial surgery simulations.

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. 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. 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 4.7mm average mean 3D surface distance. Given a geometry of an individual rib cage, the rotational errors for the pose reconstruction range from 0.1° to 0.9°. 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.

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

A statistical 3D shape model of a regularly developed human mandible is presented. The shape model (often referred to as an atlas) is created from 11 tomographic data sets, representing a mean mandibular shape including all of its variations to any shape of the underlying training set. The atlas will serve as a foundation for the reconstruction of missing or malformed bony structures. First preliminary results are presented for three different cases of distinct mandibular deformities, and it is shown, that such a 3D shape model might provide a reasonable basis for the planning of a surgical reconstruction. Hence, the atlas will be extended by at least 140 available data sets that have been acquired using a NewTom DVT.

Stefan Zachow

Papers

Finite-Element Simulation of Soft Tissue Deformation

Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data

Anatomy- and physics-based facial animation for craniofacial surgery simulations.

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

Reconstruction of mandibular dysplasia using a statistical 3D shape model