The assessment of lymph node metastases is critical for accurate cancer staging and consequently the decision for treatment options. Lymph node staging is a challenging, time-consuming task due to the fact that lymph nodes have ill-defined borders as well as varying sizes and morphological characteristics. The purpose of this study is to evaluate the effects of using different anatomical priors with the aim of guiding network attention within the application of segmentation of pathological lymph nodes in the mediastinum. The first presented prior, a distance map, displays the distance to a commonly defined point across all patients and, thus, provides an orientation of where a patch is extracted from. The second prior option, a probabilistic lymph node atlas, provides a map of areas where healthy and pathological lymph nodes are located, but also highlights lymph node stations that are more likely to become malignant. The distance map as well as the probabilistic lymph node atlas are results of an upstream atlas-to-patient registration approach. The third prior is a combination of segmentation masks of anatomical structures generated by the TotalSegmentator algorithm. A paired t-test on 5-fold cross validated results shows no significant differences in Dice score between models trained with the distance map or/and the probabilistic lymph node atlas compared to models trained with CT only. Counterintuitively, the models trained with segmentation masks of selected anatomical structures show significantly decreased segmentation accuracy. However, using the probabilistic lymph node atlas reduces the number of false negatives and consistently elevates the effect of post-processing.
The correct pose of the patient during radiography is of critical importance to ensure an adequate diagnostic quality of radiographs, which are the basis for diagnosis and treatment planning. However, correct patient positioning is not a standardized process, often resulting in inadequate radiographs and repeated radiation exposure. We propose a novel approach using Time-of-Flight cameras to assess the patient’s pose and therefore predict the expected diagnostic quality of the radiograph, before it is even captured. As a first step towards this goal, we acquired a new dataset, consisting of depth images and corresponding radiographs of the ankle using two anatomical preparations in multiple poses. The radiographs were labeled by radiologists for their diagnostic quality related to the patient’s pose. These labels serve as quality label for the corresponding pose. Using this dataset we trained deep neural networks and were able to correctly assess the diagnostic quality of a pose with a mean accuracy of up to 90.2%, demonstrating that shared features for pose assessment across patients exist and can be learned.
Currently, uoroscopy and conventional digital subtraction angiography are used for imaging guidance in endovascular aortic repair (EVAR) procedures. Drawbacks of these image modalities are X-ray exposure, the usage of contrast agents and the lack of depth information. To overcome these disadvantages, a catheter prototype containing a multicore fiber with fiber Bragg gratings for shape sensing and three electromagnetic (EM) sensors for locating the shape was built in this study. Furthermore, a model for processing the input data from the tracking systems to obtain the located 3D shape of the first 38 cm of the catheter was introduced: A spatial calibration between the optical fiber and each EM sensor was made in a calibration step and used to obtain the located shape of the catheter in subsequent experiments. The evaluation of our shape localization method with the catheter prototype in different shapes resulted in average errors from 0.99 to 2.29 mm and maximum errors from 1.73 to 2.99 mm. The experiments showed that an accurate shape localization with a multicore fiber and three EM sensors is possible, and that this catheter guidance is promising for EVAR procedures. Future work will be focused on the development of catheter guidance based on shape sensing with a multicore fiber, and the orientation and position of less than three EM sensors.
Recent developments in MRI enable the acquisition of image sequences with high spatio-temporal resolution. Cardiac motion can be captured without gating and triggering. Image size and contrast relations differ from conventional cardiac MRI cine sequences requiring new adapted analysis methods. We suggest a novel segmentation approach utilizing contrast invariant polar scanning techniques. It has been tested with 20 datasets of arrhythmia patients. The results do not differ significantly more between automatic and manual segmentations than between observers. This indicates that the presented solution could enable clinical applications of real-time MRI for the examination of arrhythmic cardiac motion in the future.
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