{"_buckets": {"deposit": "5566dfec-7bdf-4ddc-9b75-2a341ec63046"}, "_deposit": {"id": "29809", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "29809"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00029809"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2019-12", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "12", "bibliographicPageEnd": "2081", "bibliographicPageStart": "2069", "bibliographicVolumeNumber": "14", "bibliographic_titles": [{"bibliographic_title": "International Journal of Computer Assisted Radiology and Surgery"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "Purpose: The purpose of this paper is to present a fully automated abdominal artery segmentation method from a CT volume. Three-dimensional (3D) blood vessel structure information is important for diagnosis and treatment. Information about blood vessels (including arteries) can be used in patient-specific surgical planning and intra-operative navigation. Since blood vessels have large inter-patient variations in branching patterns and positions, a patient-specific blood vessel segmentation method is necessary. Even though deep learning-based segmentation methods provide good segmentation accuracy among large organs, small organs such as blood vessels are not well segmented. We propose a deep learning-based abdominal artery segmentation method from a CT volume. Because the artery is one of small organs that is difficult to segment, we introduced an original training sample generation method and a three-plane segmentation approach to improve segmentation accuracy. Method: Our proposed method segments abdominal arteries from an abdominal CT volume with a fully convolutional network (FCN). To segment small arteries, we employ a 2D patch-based segmentation method and an area imbalance reduced training patch generation (AIRTPG) method. AIRTPG adjusts patch number imbalances between patches with artery regions and patches without them. These methods improved the segmentation accuracies of small artery regions. Furthermore, we introduced a three-plane segmentation approach to obtain clear 3D segmentation results from 2D patch-based processes. In the three-plane approach, we performed three segmentation processes using patches generated on axial, coronal, and sagittal planes and combined the results to generate a 3D segmentation result. Results: The evaluation results of the proposed method using 20 cases of abdominal CT volumes show that the averaged F-measure, precision, and recall rates were 87.1%, 85.8%, and 88.4%, respectively. This result outperformed our previous automated FCN-based segmentation method. Our method offers competitive performance compared to the previous blood vessel segmentation methods from 3D volumes. Conclusions: We developed an abdominal artery segmentation method using FCN. The 2D patch-based and AIRTPG methods effectively segmented the artery regions. In addition, the three-plane approach generated good 3D segmentation results.", "subitem_description_type": "Abstract"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "Springer"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1007/s11548-019-02062-5", "subitem_relation_type_select": "DOI"}}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "\u201cThis is a post-peer-review, pre-copyedit version of an article published in [International Journal of Computer Assisted Radiology and Surgery]. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11548-019-02062-5\u201d."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "author"}]}, "item_10_source_id_61": {"attribute_name": "ISSN\uff08print\uff09", "attribute_value_mlt": [{"subitem_source_identifier": "1861-6410", "subitem_source_identifier_type": "ISSN"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Oda, Masahiro"}], "nameIdentifiers": [{"nameIdentifier": "97409", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Roth, Holger R."}], "nameIdentifiers": [{"nameIdentifier": "97410", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Kitasaka, Takayuki"}], "nameIdentifiers": [{"nameIdentifier": "97411", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Misawa, Kazunari"}], "nameIdentifiers": [{"nameIdentifier": "97412", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Fujiwara, Michitaka"}], "nameIdentifiers": [{"nameIdentifier": "97413", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Mori, Kensaku"}], "nameIdentifiers": [{"nameIdentifier": "97414", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2020-12-01"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "ijcars_arterysegmentation10_utf8_revised3rd_nocorrection.pdf", "filesize": [{"value": "4.9 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 4900000.0, "url": {"label": "ijcars_arterysegmentation10_utf8_revised3rd_nocorrection", "url": "https://nagoya.repo.nii.ac.jp/record/29809/files/ijcars_arterysegmentation10_utf8_revised3rd_nocorrection.pdf"}, "version_id": "dbfcc131-d606-430f-bd4f-831e5ea695bd"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "Abdominal artery", "subitem_subject_scheme": "Other"}, {"subitem_subject": "CT image", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Segmentation", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Fully convolutional network", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Abdominal artery segmentation method from CT volumes using fully convolutional neural network", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Abdominal artery segmentation method from CT volumes using fully convolutional neural network"}]}, "item_type_id": "10", "owner": "1", "path": ["312/313/314"], "permalink_uri": "http://hdl.handle.net/2237/00031995", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_name_i18n": "\u516c\u958b\u65e5", "attribute_value": "2020-04-03"}, "publish_date": "2020-04-03", "publish_status": "0", "recid": "29809", "relation": {}, "relation_version_is_last": true, "title": ["Abdominal artery segmentation method from CT volumes using fully convolutional neural network"], "weko_shared_id": null}