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The assessment of lymph nodes in CT examinations of cancer patients is essential for cancer staging with direct impact on therapeutic decisions. Automated detection and segmentation of lymph nodes is challenging, especially, due to significant variability in size, shape and location coupled with weak and variable image contrast. In this paper, we propose a joint detection and segmentation approach using a fully convolutional neural network based on 3D foveal patches. To enable network training, 89 publicly available CT data sets were carefully re-annotated yielding an extensive set of 4351 voxel-wise segmentations of thoracic lymph nodes. Based on these annotations, the 3D network was trained to perform per voxel classification. For enlarged potentially malignant lymph nodes, a detection rate of 79% with 8.0 false-positive detections per volume was obtained. A DICE of 0.44 was achieved on average.

Computed tomography is the modality of choice for poly-trauma patients to assess rapidly skeletal and vascular integrity of the whole body. Often several scans with and without contrast medium or with different spatial resolution are acquired. Efficient reading of the resulting extensive set of image data is vital, since it is often time critical to initiate the necessary therapeutic actions. A set of automatically found landmarks can facilitate navigation in the data and enables anatomy oriented viewing. Following this intention, we selected a comprehensive set of 17 skeletal and 5 aortic landmarks. Landmark localization models for the Discriminative Generalized Hough Transform (DGHT) were automatically created based on a set of about 20 training images with ground truth landmark positions. A hierarchical setup with 4 resolution levels was used. Localization results were evaluated on a separate test set, consisting of 50 to 128 images (depending on the landmark) with available ground truth landmark locations. The image data covers a large amount of variability caused by differences of field-of-view, resolution, contrast agent, patient gender and pathologies. The median localization error for the set of aortic landmarks was 14.4 mm and for the set of skeleton landmarks 5.5 mm. Median localization errors for individual landmarks ranged from 3.0 mm to 31.0 mm. The runtime performance for the whole landmark set is about 5s on a typical PC.