Abstract: Combined 3D Dataset for CT- and Point Cloud-based Intra-patient Lung Registration Lung250M-4B

Intra-patient lung registration aims to find correspondences between lung images of different respiratory phases, aiding in, e.g., the diagnosis of COPD, estimation of tumour motion in radiotherapy planning or tracking of lung nodules.With recent developments, deep learning-based methods are competing for state-of-the-art in various image registration tasks. Additionally, geometric deep learning on point clouds – in particular learning-based point cloud registration – shows great potential regarding computational efficiency, robustness, and anonymity preservation. Publicly available image datasets for intra-patient lung registration, however, are often not sufficiently large to train deep learning methods properly or include primarily small motions, which transfer poorly to larger deformations. When purely using point cloud data, on the other hand, a fair comparison with state-of-the-art image-based registration methods is not possible and for both expert supervision is desirable. With Lung250M-4B [1], we present a dataset, that aims to tackle these problems. It consists of 248 curated and pre-processed public multi-centric in- and expiratory lung CT scans from 124 patients with large motion between scans. It comprises the DIR-LAB COPDgene [2] data as test data, which is popularly used to evaluate registration methods. Moreover, for each image, corresponding vessel point clouds are provided. For supervision, vein and artery segmentations as well as thousands of image-derived keypoint correspondences are included. Multiple validation scan pairs are annotated with manual landmarks.With all of this, Lung250M- 4B is the first dataset to enable a fair comparison between image- and point cloud-based registration methods, while consisting of significantly more image pairs than previous lung CT datasets, and it contains accurate correspondences for supervised learning. The download link for the data, processing scripts and benchmark results are available under https://github.com/multimodallearning/Lung250M-4B.