{"url":"/dataset/intra","name":"IntrA","full_name":null,"description_markdown":"**IntrA** is an open-access 3D intracranial aneurysm dataset that makes the application of points-based and mesh-based classification and segmentation models available. This dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction.\r\n\r\n103 3D models of entire brain vessels are collected by reconstructing scanned 2D MRA images of patients (the raw 2D MRA images are not published due to medical ethics).\r\n1909 blood vessel segments are generated automatically from the complete models, including 1694 healthy vessel segments and 215 aneurysm segments for diagnosis.\r\n116 aneurysm segments are divided and annotated manually by medical experts; the scale of each aneurysm segment is based on the need for a preoperative examination.\r\nGeodesic distance matrices are computed and included for each annotated 3D segment, because the expression of the geodesic distance is more accurate than Euclidean distance according to the shape of vessels.\r\n\r\nSource: [https://github.com/intra3d2019/IntrA](https://github.com/intra3d2019/IntrA)\nImage Source: [https://github.com/intra3d2019/IntrA](https://github.com/intra3d2019/IntrA)","description_withheld":null,"homepage":"https://github.com/intra3d2019/IntrA","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/intra-3d-intracranial-aneurysm-dataset-for","title":"IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning","first_author":"Xi Yang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"3D Point Cloud Classification","url":"/task/3d-point-cloud-classification","datasets_with_task":"/datasets/task/3d-point-cloud-classification"},{"name":"Medical Diagnosis","url":"/task/medical-diagnosis","datasets_with_task":"/datasets/task/medical-diagnosis"},{"name":"3D Part Segmentation","url":"/task/3d-part-segmentation","datasets_with_task":"/datasets/task/3d-part-segmentation"}],"languages":[],"variants":["IntrA"],"data_loaders":[{"repo":"https://github.com/intra3d2019/IntrA","url":"https://github.com/intra3d2019/IntrA","frameworks":[]}],"num_papers_in_archive":27,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-point-cloud-classification-on-intra","task":"3D Point Cloud Classification","dataset_variant":"IntrA","rows":12,"metrics":["F1 score (5-fold)"],"first_row_in_archive_order":{"model":"3DMedPT","paper":"/paper/3d-medical-point-transformer-introducing","metrics":{"F1 score (5-fold)":"0.936"},"code_links":[{"title":"crane-papercode/3dmedpt","url":"https://github.com/crane-papercode/3dmedpt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-part-segmentation-on-intra","task":"3D Part Segmentation","dataset_variant":"IntrA","rows":7,"metrics":["IoU (V)","IoU (A)","DSC (V)","DSC (A)"],"first_row_in_archive_order":{"model":"3DMedPT","paper":"/paper/3d-medical-point-transformer-introducing","metrics":{"DSC (A)":"89.71","DSC (V)":"97.29","IoU (A)":"82.39","IoU (V)":"94.82"},"code_links":[{"title":"crane-papercode/3dmedpt","url":"https://github.com/crane-papercode/3dmedpt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/3d-medical-point-transformer-introducing","title":"3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis","date":"2021-12-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/adaptive-graph-convolution-for-point-cloud","title":"Adaptive Graph Convolution for Point Cloud Analysis","date":"2021-08-18","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/paconv-position-adaptive-convolution-with","title":"PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds","date":"2021-03-26","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pct-point-cloud-transformer","title":"PCT: Point cloud transformer","date":"2020-12-17","rows_on_this_dataset":1,"code_links":11,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/geometry-sharing-network-for-3d-point-cloud","title":"Geometry Sharing Network for 3D Point Cloud Classification and Segmentation","date":"2019-12-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pointcnn-convolution-on-x-transformed-points","title":"PointCNN: Convolution On X-Transformed Points","date":"2018-12-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pointconv-deep-convolutional-networks-on-3d","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","date":"2018-11-17","rows_on_this_dataset":2,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":11,"samples_unverified":4,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spidercnn-deep-learning-on-point-sets-with","title":"SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters","date":"2018-03-30","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":2,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/so-net-self-organizing-network-for-point","title":"SO-Net: Self-Organizing Network for Point Cloud Analysis","date":"2018-03-12","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dynamic-graph-cnn-for-learning-on-point","title":"Dynamic Graph CNN for Learning on Point Clouds","date":"2018-01-24","rows_on_this_dataset":1,"code_links":21,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":44,"samples_ran":16,"samples_unverified":28,"pointer_only_for_licence":31,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pointcnn-convolution-on-mathcalx-transformed","title":"PointCNN: Convolution On $\\mathcal{X}$-Transformed Points","date":"2018-01-23","rows_on_this_dataset":1,"code_links":16,"syntology":null},{"paper":"/paper/pointnet-deep-hierarchical-feature-learning","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","date":"2017-06-07","rows_on_this_dataset":2,"code_links":68,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":67,"samples_ran":36,"samples_unverified":31,"pointer_only_for_licence":26,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pointnet-deep-learning-on-point-sets-for-3d","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","date":"2016-12-02","rows_on_this_dataset":2,"code_links":110,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":164,"samples_ran":89,"samples_unverified":75,"pointer_only_for_licence":90,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":9,"samples_harvested":314,"samples_ran":161,"samples_unverified":153,"pointer_only_for_licence":150,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}