{"url":"/dataset/brain-us","name":"Brain US","full_name":null,"description_markdown":"This brain anatomy segmentation dataset has 1300 2D US scans for training and 329 for testing. A total of 1629 in vivo B-mode US images were obtained from 20 different subjects (age<1 years old) who were treated between 2010 and 2016. The dataset contained subjects with IVH and without (healthy subjects but in risk of developing IVH). The US scans were collected using a Philips US machine with a C8-5 broadband curved array transducer using coronal and sagittal scan planes. For every collected image ventricles and septum pellecudi are manually segmented by an expert ultrasonographer. We split these images randomly into 1300 Training images and 329 Testing images for experiments. Note that these images are of size 512 × 512. \r\n\r\nSource: [Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data](https://arxiv.org/pdf/1912.08364.pdf)\r\n\r\nImage source: [Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data](https://arxiv.org/pdf/1912.08364.pdf)","description_withheld":null,"homepage":"https://arxiv.org/pdf/1912.08364.pdf","introduced_date":"2019-12-18","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-to-segment-brain-anatomy-from-2d","title":"Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data","first_author":"Jeya Maria Jose V.","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"}],"languages":[],"variants":["Brain US"],"data_loaders":[{"repo":"https://github.com/enzoephrem/DeepCancer","url":"https://github.com/enzoephrem/DeepCancer","frameworks":["tf","pytorch"]}],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-brain-us","task":"Medical Image Segmentation","dataset_variant":"Brain US","rows":3,"metrics":["F1","IoU"],"first_row_in_archive_order":{"model":"MedT","paper":"/paper/medical-transformer-gated-axial-attention-for","metrics":{"F1":"88.84","IoU":"81.34 "},"code_links":[{"title":"jeya-maria-jose/Medical-Transformer","url":"https://github.com/jeya-maria-jose/Medical-Transformer"},{"title":"dani-capellan/ptb_lungregionextractor","url":"https://github.com/dani-capellan/ptb_lungregionextractor"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/medical-transformer-gated-axial-attention-for","title":"Medical Transformer: Gated Axial-Attention for Medical Image Segmentation","date":"2021-02-21","rows_on_this_dataset":3,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}