Abstract
Localization of tube-shaped objects is an important topic in medical imaging. Previously it was mainly addressed via dense segmentation that may produce inconsistent results for long and narrow objects. In our work, we propose a point-based approach for explicit centerline segmentation that can be learned by fully-convolutional networks. We propose a new bi-directional encoding scheme that does not require any autoregressive blocks and is robust to various shapes and orientations of lines, being adaptive to the number of points in their centerlines. We present extensive evaluation of our approach on synthetic and real data (chest x-ray and coronary angiography) and show its advantage over the state-of-the-art segmentation models.
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Sirazitdinov, I., Saalbach, A., Schulz, H., Dylov, D.V. (2022). Bi-directional Encoding for Explicit Centerline Segmentation by Fully-Convolutional Networks. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_66
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