# Coronary Centerline Extraction from CCTA Using 3D-UNet
## 6 Conclusions  
We have provided an open source (available at [39]) implementation of a 3D U-Net, suitable for segmenting the coronary artery centerline. We have built upon a 2D U-Net-base implementation network, originally designed for retina vessel segmentation, and developed a 3D version capable of segmenting with voxel precision. We have conceived a novel loss function designed to combat two simultaneous problems, usually related to volumetric medical segmentation: huge class imbalance and sparse annotation. Without this loss function, the convergence of the model was not guaranteed even with using augmented inputs. We have built a customizable tool to generate annotated ground truth for the Rotterdam Coronary Artery Centerline Extraction from reference files. We have integrated a 3D augmentation algorithm [37] with the dataset to combat the problem of very few training examples. Without integrating one, the model would not generalize the centerline extraction. We have demonstrated that the training convergence using a second loss function starting with a network pretrained with another loss function. Without the pretraining, training directly with the second loss function would not converge. As for future improvements, the existing algorithm could benefit from using transfer learning, which was previously applied for U-Net architectures in [40], or for medical image classification models [41]. Even if the training dataset is small for coronary artery centerlines, one can use other segmentation tasks to pretrain the model. Using deconvolution layers instead of upsampling layers in the proposed U-Net architecture may improve the pixel-wise accuracy of the segmentation. The results can be perfected by incorporating a wider training set with data from multiple benchmarks in the same way as in [14; 16; 17]. Cardiovascular diseases (CVDs), classified as a group of disorders, are the number one cause of death globally [42]. Automated extraction of information about the coronary artery is one step in the computer-aided diagnosis of CVDs. Based on the extracted centerlines, other methods can be employed for visualizations, artery segmentation, plaque detection, and stenosis evaluation. The proposed method can help in improving the visualization, detection, and segmentation of various features such as soft plaque or calcified plaque if included in a stenosis score processing pipeline. Conceptualization, A.D., V.O. and R.B. ; methodology, A.D. and V.O. ; software, A.D.; validation, A.D.; formal analysis, A.D.; investigation, A.D.; resources, A.D.; data curation, A.D.; writing--original draft preparation, A.D.; writing--review and editing, A.D.; visualization, A.D.; su-pervision, R.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to data confidentiality. The authors declare no conflict of interest. Starting from a randomly initialized model, with a dice coefficient loss function, which optimizes the tradeoff between precision and recall, the training converges quickly to predicting nothing. This happens because of two reasons--the huge class imbalance and the sparse annotation of the centerlines, where the model receives mixed signals about what represents a centerline and what does not. A different validation approach was used by starting to train the model with the proposed loss function, and when the model is sufficiently trained, switching to dice loss. The training now converges, and an increase in the dice coefficient can be seen in Figure A2. Of course, this is not necessarily the objective since the data are sparsely annotated, but this is another confirmation that the results are on the right track. We further present the results in terms of dice coefficient after switching the training objective.