تقرير
LitCall: Learning Implicit Topology for CNN-based Aortic Landmark Localization
| العنوان: | LitCall: Learning Implicit Topology for CNN-based Aortic Landmark Localization |
|---|---|
| المؤلفون: | Bian, Zhangxing, Zhong, Jiayang, Lu, Yanglong, Hatt, Charles R., Burris, Nicholas S. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing |
| الوصف: | Landmark detection is a critical component of the image processing pipeline for automated aortic size measurements. Given that the thoracic aorta has a relatively conserved topology across the population and that a human annotator with minimal training can estimate the location of unseen landmarks from limited examples, we proposed an auxiliary learning task to learn the implicit topology of aortic landmarks through a CNN-based network. Specifically, we created a network to predict the location of missing landmarks from the visible ones by minimizing the Implicit Topology loss in an end-to-end manner. The proposed learning task can be easily adapted and combined with Unet-style backbones. To validate our method, we utilized a dataset consisting of 207 CTAs, labeling four landmarks on each aorta. Our method outperforms the state-of-the-art Unet-style architectures (ResUnet, UnetR) in terms of localization accuracy, with only a light (#params=0.4M) overhead. We also demonstrate our approach in two clinically meaningful applications: aortic sub-region division and automatic centerline generation. Comment: Accepted to Medical Imaging 2022: Image Processing |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2304.07607 |
| رقم الأكسشن: | edsarx.2304.07607 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |