{"url":"/dataset/meis","name":"MEIS","full_name":"M-mode Echocardiograms for Instance Segmentation","description_markdown":"MEIS comprises a total of 2,639 images in the size of 1024 × 768 toward two recording views (Aortic Valve (AV) and\r\nLeft Ventricle (LV)) with 1,521 (747 in AV + 774 in LV) images for training and 1,118 (559 in AV + 559 in LV) for\r\ntesting, respectively. Each view must be detected with two objects to calculate the measurement indicators. That is in\r\ntotal with four object classes (two objects in each view): aortic root (AoR) and left atrium (LA) in AV; interventricular\r\nseptum (IVS) and left ventricular posterior wall (LVPW) in LV. The medical meaning and purpose of each indicator are\r\nlisted in the following:\r\n• AV: LA-Dimension and AoR-Dimension can be measured for calculating different indicators, such as AoR/LA\r\nratio, to examine the state of the aortic valve.\r\n• LV: 6 measurements include IVSs, IVSd, LVIDs, LVIDd, LVPWs, and LVPWd. These concerned thicknesses\r\nand dimensions in LV recording are used to estimate other cardiac functions through specific medical formulas,\r\nincluding LV mass, LV ejection fraction, end-diastolic volume, end-systolic volume, and more.","description_withheld":null,"homepage":"https://drive.google.com/drive/folders/1Ve3UC9pP-FO5wN5MLB9OiKBAs7xAGkeN?usp=sharing","introduced_date":"2023-08-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/real-time-automatic-m-mode-echocardiography","title":"Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels","first_author":"Ching-Hsun Tseng","url":null},"license":null,"modalities":[],"tasks":[{"name":"Real-time Instance Segmentation","url":"/task/real-time-instance-segmentation","datasets_with_task":"/datasets/task/real-time-instance-segmentation"},{"name":"Real-time instance measurement","url":"/task/real-time-instance-measurement","datasets_with_task":"/datasets/task/real-time-instance-measurement"}],"languages":[],"variants":["MEIS"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/real-time-instance-segmentation-on-meis","task":"Real-time Instance Segmentation","dataset_variant":"MEIS","rows":2,"metrics":["FLOPs (G)","Frame (fps)","Size (M)","avgAP (mask AP + box AP)","boxAP","maskAP"],"first_row_in_archive_order":{"model":"maYOLACT ResNet50","paper":"/paper/real-time-automatic-m-mode-echocardiography","metrics":{"FLOPs (G)":"0.4826","Frame (fps)":"36.13","Size (M)":"30.38","avgAP (mask AP + box AP)":"46.29","boxAP":"49.59","maskAP":"42.99"},"code_links":[{"title":"hanktseng131415go/ramem","url":"https://github.com/hanktseng131415go/ramem"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/real-time-automatic-m-mode-echocardiography","title":"Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels","date":"2023-08-15","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}