To support the development of an automatic path-planning procedure for bronchoscopy, semantic segmentation of pulmonary nodules and airways is required. The segmentation should happen simultaneously and automatically to save time and effort during the intervention. The challenges of the combined segmentation are the different shapes, frequencies, and sizes of airways, lungs, and pulmonary nodules. Therefore, a sampling strategy is explored using especially relevant crops of the volumes during training and weighting the classes differently, counteracting class imbalance. For the segmentation, a 3D U-Net is used. The proposed algorithm is compared to nnU-Net. First, it is trained as a one-class problem on all classes individually and in a second approach as a multi-label problem. The developed Multi-Label Segmentation network (MLS) is trained with full supervision. The results of the experiments have shown that without further adaption, a combined segmentation of nodules, airways, and lungs is complex. The multi-label nnU-Net failed to find nodules. Considering the different properties of the three classes, MLS accomplishes segmenting all classes simultaneously.
For many medical questions, X-ray imaging belongs to the gold standard for diagnosis, treatment planning, treatment guidance, and surgery assessment. To improve the reading performance, standardized image rotation is an important step. We propose a new algorithm to estimate the correct image rotation. For many body regions, one line can be defined that is aligned with the upright orientation of the X-ray image. This line can be, for example, the shaft axis of a long bone or the axis of the spine. In this paper, we propose a strategy to automatically align X-ray images with their standard orientation. In a first step, the heatmap of this line is determined using the segmentation network D-LinkNet. The rotation direction, up to a top-down flip, is obtained by computing the orientation of the main axis of this heatmap. For the orientation computation, we compare PCA and Hu moments. While the PCA requires to threshold the heatmap, Hu moments can be used directly on the output values of the network, preserving the (un)certainty of the segmentation. We compare these two methods with a ResNet-18 for the direct estimation of the image rotation on 220 X-ray images from the MURA dataset showing the wrist in the AP view. With the heatmap-based approach followed by Hu moments analysis, the median absolute error for the angle estimation can be reduced to 0.7° compared to 1.7° by a direct estimation method. PCA suffers from noisy heatmaps for images of bad quality degrading the overall performance of this approach.
The potential benefit of hybrid X-ray and MR imaging in the interventional environment is large due to the combination of fast imaging with high contrast variety. However, a vast amount of existing image enhancement methods requires the image information of both modalities to be present in the same domain. To unlock this potential, we present a solution to image-to-image translation from MR projections to corresponding x-ray projection images. The approach is based on a state-of-the-art image generator network that is modified to fit the specific application. Furthermore, we propose the inclusion of a gradient map in the loss function to allow the network to emphasize high-frequency details in image generation. Our approach is capable of creating x-ray projection images with natural appearance. Additionally, our extensions show clear improvement compared to the baseline method.
Cardiovascular diseases are the major cause of death worldwide. Magnetic resonance imaging (MRI) is often used for the diagnosis of cardiac diseases because of its good soft tissue contrast. Furthermore, the fibrosis characterization of the myocardium can be important for accurate diagnosis and treatment planning. The clinical gold standard to visualize myocardial scarring is late gadolinium enhanced (LGE) MRI. However, the challenge arises in the accurate segmentation of the endocardial and epicardial border because of the smooth transition between the blood pool and scarred myocardium, as contrast agent accumulates in the damaged tissue and leads to hyper-enhancements. An exact segmentation, is essential for the scar tissue quantification. We propose a deep learning-based method to segment the left ventricle’s endocardium and epicardium in LGE-MRI. To this end, a multi-scale fully convolutional neural network with skip-connections (U-Net) and residual units is applied to solve the multiclass segmentation problem. As a loss function, weighted cross-entropy is used. The network is trained on 70 clinical LGE MRI sequences, validated with 5, and evaluated with 26 data sets. The approach yields a mean Dice coefficient of 0.90 for the endocard and 0.87 for the epicard. The proposed method segments the endocardium and epicardium of the left ventricle fully automatically with a high accuracy.
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