This paper evaluates strategies for atlas selection in atlas-based segmentation of three-dimensional biomedical images. Segmentation by intensity-based non-rigid registration to atlas images is applied to confocal microscopy images acquired from the brains of 20 bees. This paper evaluates and compares four di#erent approaches for atlas image selection: registration to an individual atlas image (IND), registration to an average shape atlas image (AVG), registration to the most similar image from a database of individual atlas images (SIM), and registration to all images from a database of individual atlas images with subsequent multi-classifier decision fusion (MUL). The MUL strategy is a novel application of multi-classifier techniques, which are common in pattern recognition, to atlas-based segmentation. For each atlas selection strategy, the segmentation performance of the algorithm was quantified by means of the similarity index between the automatic segmentation result and a manually generated gold standard.

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Abstract. In this paper we propose an approach to generate a 4D statistical model of respiratory lung motion based on thoracic 4D CT data of different patients. A symmetric diffeomorphic intensity–based registration technique is used to estimate subject–specific motion models and to establish inter–subject correspondence. The statistics on the diffeomorphic transformations are computed using the Log–Euclidean framework. We present methods to adapt the genererated statistical 4D motion model to an unseen patient–specific lung geometry and to predict individual organ motion. The prediction is evaluated with respect to landmark and tumor motion. Mean absolute differences between model–based predicted landmark motion and corresponding breathing–induced landmark displacements as observed in the CT data sets are 3.3 ± 1.8 mm considering motion between end expiration to end inspiration, if lung dynamics are not impaired by lung disorders. The statistical respiratory motion model presented is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in the fields of radiation therapy and image guided diagnosis. 1

Modeling of respiratory motion gains in importance within the field of radiation therapy of lung cancer patients. Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient’s anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D CT data of different patients to extend motion modeling capabilities. Our modeling process consists of two main parts: an intra–subject registration to generate subject–specific motion models and an inter–subject registration to combine these subject–specific motion models into a mean motion model. Further, we present methods to adapt the mean motion model to a patient-specific lung geometry. A first evaluation of the model was done by using the generated mean motion model to predict lung and tumor motion of individual patients and comparing the prediction quality to non–linear registration. Our results show that the average difference in prediction quality (measured by overlap coefficients) between non–linear registration and model–based prediction is approx. 10%. However, the patient–specific registration relies on individual 4D image data, whereas the model–based prediction was obtained without knowledge of the individual breathing dynamics. Results show that the model predicts motion patterns of individual patients generally well and we conclude from our results that such a model has the capability to provide valuable a-priori knowledge in many fields of applications. Categories and Subject Descriptors (according to ACM CCS): G.3 [Probability and Statistics]: Time series analysis 1.

journal homepage: www.elsevier.com/locate/media Location registration and recognition (LRR) for serial analysis of nodules

One important aspect of lung cancer staging is the assessment of mediastinal lymph nodes in 3-D chest computed tomography (CT) images. In the current clinical routine this is done manually by analyzing the 3-D CT image slice by slice to find nodes, evaluate them quantitatively, and assign labels to them for describing the clinical and pathologic extent of metastases. In this paper we present a method to automate the process of lymph node detection and labeling by creation of a mediastinal average image and a novel lymph node atlas containing probability maps for mediastinal, aortic, and N1 nodes. Utilizing a fast deformable registration approach to match the atlas with CT images of new patients, our method can maintain an acceptable runtime. In comparison to previously published methods for mediastinal lymph node detection and labeling it also shows a good sensitivity and positive predictive value.