{"doi":"10.1007/978-3-030-00889-5_1","title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","abstract":"In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar. We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos. Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively.","journal":"Lecture Notes in Computer Science","year":2018,"id":6574,"datarank":1.3608168379039112,"base_score":9.07211225269274,"endowment":9.07211225269274,"self_citation_contribution":1.3608168379039112,"citation_network_contribution":0.0,"self_endowment_contribution":1.3608168379039112,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8708,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0502,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2018-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":60652,"name":"Md Mahfuzur Rahman Siddiquee","orcid":"0000-0001-7145-215X","position":1,"is_corresponding":false},{"id":60653,"name":"Nima Tajbakhsh","orcid":"0000-0001-8614-4811","position":2,"is_corresponding":false},{"id":60654,"name":"Jianming Liang","orcid":"0000-0001-5486-1613","position":3,"is_corresponding":false},{"id":60651,"name":"Zongwei Zhou","orcid":"0000-0002-3154-9851","position":0,"is_corresponding":true}],"reference_count":11,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}