The aim of this study is to assess the benefits of deep learning (DL) for individual tree detection from high-density airborne LiDAR data in a complex Alpine forest ecosystem. We transformed the point cloud into 2.5D data sets and used several data augmentation procedures to deal with the sparse reference data for YoloR and ScaledYolo deep neural networks (DNN). We found that the correct detection rate is up to 15% higher for ScaledYolo compared to YoloR, but at the cost of a higher commission error. ScaledYolo outperforms traditional approaches by 20% higher detection rate. However, future research needs to deal with commission errors and to better separate the effect of sparse reference data from the intrinsic DNN accuracy.

The automatic extraction and labeling of the rib centerlines is a useful yet challenging task in many clinical applications. In this paper, we propose a new approach integrating rib seed point detection and template matching to detect and identify each rib in chest CT scans. The bottom-up learning based detection exploits local image cues and top-down deformable template matching imposes global shape constraints. To adapt to the shape deformation of different rib cages whereas maintain high computational efficiency, we employ a Markov Random Field (MRF) based articulated rigid transformation method followed by Active Contour Model (ACM) deformation. Compared with traditional methods that each rib is individually detected, traced and labeled, the new approach is not only much more robust due to prior shape constraints of the whole rib cage, but removes tedious post-processing such as rib pairing and ordering steps because each rib is automatically labeled during the template matching. For experimental validation, we create an annotated database of 112 challenging volumes with ribs of various sizes, shapes, and pathologies such as metastases and fractures. The proposed approach shows orders of magnitude higher detection and labeling accuracy than state-of-the-art solutions and runs about 40 seconds for a complete rib cage on the average.

Fluoroscopic overlay images rendered from preoperative volumetric data can provide additional anatomical details to guide physicians during catheter ablation procedures for treatment of atrial fibrillation (AFib). As these overlay images are often compromised by cardiac and respiratory motion, motion compensation methods are needed to keep the overlay images in sync with the fluoroscopic images. So far, these approaches have either required simultaneous biplane imaging for 3-D motion compensation, or in case of monoplane X-ray imaging, provided only a limited 2-D functionality. To overcome the downsides of the previously suggested methods, we propose an approach that facilitates a full 3-D motion compensation even if only monoplane X-ray images are available. To this end, we use a training phase that employs a biplane sequence to establish a patient specific motion model. Afterwards, a constrained model-based 2-D/3-D registration method is used to track a circumferential mapping catheter. This device is commonly used for AFib catheter ablation procedures. Based on the experiments on real patient data, we found that our constrained monoplane 2-D/3-D registration outperformed the unconstrained counterpart and yielded an average 2-D tracking error of 0.6 mm and an average 3-D tracking error of 1.6 mm. The unconstrained 2-D/3-D registration technique yielded a similar 2-D performance, but the 3-D tracking error increased to 3.2 mm mostly due to wrongly estimated 3-D motion components in X-ray view direction. Compared to the conventional 2-D monoplane method, the proposed method provides a more seamless workflow by removing the need for catheter model re-initialization otherwise required when the C-arm view orientation changes. In addition, the proposed method can be straightforwardly combined with the previously introduced biplane motion compensation technique to obtain a good trade-off between accuracy and radiation dose reduction.

Detailed and highly accurate digital maps provide useful information for future driver assistance systems. The information of the positions of infrastructure objects and lane markings can be used to extend the knowledge of the environment obtained by local sensors. To exploit highly accurate maps, the exact position of the vehicle within the map must be known. For that, a rough localization with standard GPS is extended by matching objects detected with a laser scanner and data from the map. The paper focuses on road construction sites, which are a demanding environment for sensorial perception and interpretation. The matching algorithms are based on beacons, which are commonly used infrastructure elements at road works.

Road construction sites often are the reason for traffic jams and accidents due to the reduced road width. Driver assistance systems for these demanding environments highly benefit from a digital representation of the road layout. This digital map includes all important infrastructure elements, such as barriers, temporary road markings and guiding reflector posts. The paper describes the automatic generation of a detailed and highly accurate “Road Work Map” using video camera and laser scanners.

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.

We present an algorithm for the segmentation of the liver in 2-D computed tomography slice images. The basis for our algorithm is an implicit active shape model. In order to detect the liver boundary and guide the shape model deformation, a boundary classifier has been integrated into the implicit framework in a novel manner. The accuracy of the algorithm has been evaluated for 20 test cases including both normal and abnormal livers.

The space mission TerraSAR-X is the first German space project implemented under a Public Private Partnership (PPP). Cooperation partners are the German Aerospace Centre (DLR) and EADS Astrium GmbH. Within this construct, DLR will be responsible for the scientific use of the TerraSAR-X data, whereas commercial marketing will be undertaken exclusively by Infoterra GmbH, a wholly-owned EADS Astrium subsidiary. In a co-operation between Infoterra GmbH and Joanneum Research, Value Added products and processors have been developed for TerraSAR-X data. These products are mainly oriented at the area of interest or are mapping products which represent a higher level of image processing in terms of radiometric correction and orthorectification, mosaics, subsets and merges. In this paper, these products are described. Further, an insight into the automated and semi-automated production chain is provided.

The feasibility to extract and visualise forest biometric information from Ikonos and QuickBird satellite imagery was demonstrated for a vegetated area in Portugal