{"id":45591,"date":"2023-11-20T05:22:15","date_gmt":"2023-11-20T11:22:15","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/pulmonary-nodules-segmentation\/"},"modified":"2024-10-31T12:27:08","modified_gmt":"2024-10-31T17:27:08","slug":"pulmonary-nodules-segmentation","status":"publish","type":"tcia_analysis_result","link":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/pulmonary-nodules-segmentation\/","title":{"rendered":"PULMONARY-NODULES-SEGMENTATION"},"featured_media":0,"template":"","class_list":["post-45591","tcia_analysis_result","type-tcia_analysis_result","status-publish"],"cancer_types":["Lung"],"citations":[45583,45585,9225],"result_doi":"10.7937\/K9\/TCIA.2014.V7CVH1JO","result_download_info":"","result_downloads":false,"version_change_log_archived":"Version 1 (Current): 2015\/02\/24\r\n\r\nData TypeDownload all or Query\/FilterImages\u00a0containing the 66 testing nodules that are delineated by all four board certified radiologists\u00a0(DICOM)\u00a0Images containing the 77 LIDC testing nodules\u00a0that are segmented by three or more radiologists\u00a0(DICOM)","versions":false,"additional_resources":"","cancer_locations":["Lung"],"publications_related":"The Collection authors recommend these readings to give context to this dataset","result_page_accessibility":"Public","detailed_description":"","publications_using":"TCIA maintains\u00a0a list of publications<\/a>\u00a0that leveraged this dataset. If you have a manuscript you\u2019d like to add please\u00a0contact TCIA\u2019s Helpdesk<\/a>.","result_title":"Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset","species":false,"version_number":"1","date_updated":"2015-02-24","related_collections":[42151],"result_short_title":"Pulmonary-Nodules-Segmentation","subjects":"102","related_analysis_results":false,"result_browse_title":"Pulmonary-Nodules-Segmentation","supporting_data":false,"version_change_log":"","collections":"Below is a list of the Collections used in these analyses:\r\n<\/colgroup>\r\n\r\n\r\n\r\n\r\n
Data Type<\/th>\r\nDownload<\/th>\r\nLicense<\/th>\r\n<\/tr>\r\n
Corresponding Original CT Images from LIDC-IDRI<\/a> containing the 66 testing nodules that are delineated by all four board certified radiologists (DICOM)<\/td>\r\n\r\n\r\n
Corresponding Original CT Images from LIDC-IDRI<\/a> containing the 77 LIDC testing nodules that are segmented by three or more radiologists (DICOM)<\/td>\r\n\r\n\r\n
\r\n\r\nCC BY 3.0<\/a>\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>","result_summary":"We present new pulmonary nodule segmentation algorithms for computed\u00a0tomography (CT). These include a fully--automated (FA) system, a\u00a0semi-automated (SA) system, and a hybrid system. Like most traditional\u00a0systems, the new FA system requires only a single user-supplied cue\u00a0point. On the other hand, the SA system represents a new algorithm class\u00a0requiring 8 user-supplied control points. This does increase the burden on\u00a0the user, but we show that the resulting system is highly robust and can\u00a0handle a variety of challenging cases. The proposed hybrid system starts\u00a0with the FA system. If improved segmentation results are needed, the SA\u00a0system is then deployed.\r\n\r\nThe FA segmentation engine has 2 free parameters, and the SA system has 3. These parameters are adaptively determined for each nodule in a search process guided by a regression neural network (RNN). The RNN uses a number of features computed for each candidate segmentation. We train and test our systems using the new Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI)<\/a> data. To the best of our knowledge, this is one of the first nodule-specific performance benchmarks using the new LIDC-IDRI<\/a> dataset. We also compare the performance of the proposed methods with several previously reported results on the same data used by those other methods. Our results suggest that the proposed FA system improves upon the state-of-the-art, and the SA system offers a considerable boost over the FA system.\r\n\r\nThe download links provided below provide easy access to specific subsets of images from our study, which are described in much greater detail in our publication (https:\/\/doi.org\/10.1016\/j.media.2015.02.002<\/a>).","collection_downloads":[45587,45589],"result_featured_image":false,"result_acknowledgements":"","hide_from_browse_table":"1","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45591","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_analysis_result"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45591\/revisions"}],"predecessor-version":[{"id":47461,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45591\/revisions\/47461"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=45591"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}