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In this study, a framework is developed to directly quantify left ventricle multiple indices without the process of segmentation. At first, DenseNet is utilized to extract spatial features for each cardiac frame. Then, in order to take advantage of the time sequence information, the temporal feature for consecutive frames is encoded using gated recurrent unit (GRU). After that, the attention mechanism is integrated into the decoder to effectively establish the mappings between the input sequence and corresponding output sequence. Simultaneously, a regression layer with the same decoder output is used to predict multi\u2010indices of the left ventricle. Different weights are set for different types of indices based on experience, and l2\u2010norm is used to avoid model overfitting. Compared with the state\u2010of\u2010the\u2010art (SOTA), our method can not only produce more competitive results but also be more flexible. This is because the prediction results in our study can be obtained for each frame online while the SOTA only can output results after all frames are analyzed.<\/jats:p>","DOI":"10.1155\/2021\/3260259","type":"journal-article","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T22:50:17Z","timestamp":1613861417000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi\u2010Indices Quantification for Left Ventricle via DenseNet and GRU\u2010Based Encoder\u2010Decoder with Attention"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1625-1421","authenticated-orcid":false,"given":"Zhi","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunhua","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,2,20]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10334-015-0521-4"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-015-0170-9"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.04.010"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.09.005"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2018.2879188"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2017.2778799"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180513"},{"key":"e_1_2_10_8_2","unstructured":"AbdelmaguidE. 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