Detection and quantification of atherosclerotic plaque in the coronary arteries is important in cardiovascular risk analysis. Atherosclerotic plaque can be visualized using coronary computed tomography angiography (CCTA). Manual identification and segmentation of plaque is a complex task that requires high level of expertise. Hence, several automatic approaches have been designed. Automatic methods employing deep learning have shown to outperform conventional approaches, but they are hampered by the requirement on the availability of large and diverse training data. To address this, we designed a method that synthesizes calcified and non-calcified atherosclerotic plaque in the coronary arteries. First, we generate plaque geometry using conventional image analysis approach that varies the radius, length and angle of a plaque and we use this to generate a crude inpainting of a plaque on a target artery. Thereafter, we employ a conditional generative adversarial network (GAN) to synthesize the plaque texture in CCTA. The generator is trained to generate fake images with realistic appearance. The discriminator is trained to distinguish the synthesized fake and the real images. The data set for training and evaluation of the plaque synthesis contained CCTA scans of 102 patients (50 training, 52 testing) with manually annotated calcified and non-calcified plaque. To evaluate performance of the synthesis method, we compared CCTA patches with real and synthesized plaque. The evaluation resulted in mean (standard deviation) structural similarity index of 0.99 (0.01), peak signal noise ratio of 73.99 (5.52) and mean absolute error of 5.56 (3.23) HU. To evaluate whether synthesized data enables plaque segmentation, an additional set of CCTA scans of 92 patients without visible plaque was collected. In these scans, plaque was synthesized using the developed approach, containing in total 615 calcified and 544 non-calcified plaque lesions. The synthesized data was used to train a 3D UNet for segmenting calcified and non-calcified plaque lesions. Automatic segmentation which was trained with real data only resulted in Dice coefficients of 0.68 and 0.35 for calcified and non-calcified plaque, respectively. This was significantly improved by pretraining the network with synthetic data and refining it with real data, which resulted in a Dice coefficients of 0.70 (p=0.03) and 0.36 (p=0.02) for calcified and non-calcified plaque, respectively. The results demonstrate that training with CCTA scans with automatically synthesized calcified and non-calcified plaque improves the performance of plaque segmentation.
Although MRI with hepatospecific contrast agents is a new standard diagnostic imaging for patients with liver cancer, there are no automated methods for detailed segmentation of the liver vasculature in cases with progressed tumors. This is due to the anatomical complexity, underlying disease and tunability of MRI image contrast, which challenge automatization. Here, we investigated the feasibility of liver vessel segmentation with three CNN architectures in combination with four different loss functions. In particular, a 3D Unet, a Vnet and its modification with an intra-layer dense block (DVnet) were evaluated. Dice-based loss, categorical cross entropy (CCE), weighed categorical cross entropy (WCCE) and focal loss (FL) were used as loss functions for training, the latter two to deal with the imbalanced class problem. A cohort of 90 patients (60 training, 10 validation and 20 testing) with progressed liver tumors were involved in this study, with manually annotated liver vasculature as a “gold standard”. Trained networks were evaluated by means of the Dice coefficient and centerline-based F1 score calculations. Models trained with balanced loss functions (FL,WCEE) performed the best for DVnet, while Vnet had the best performance for unbalanced loss functions. Vnet and DVnet architectures trained with an FL had the best overall segmentation accuracy (DC = 70%), while networks with a Dicebased loss had the lowest performance (max DC = 42%). In conclusion, the use of balanced loss functions, addition of an intra-layer dense block and drop-outs into the network architecture improved handling the unbalanced class problem in liver vessel segmentation.
Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.
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