{"created":"2023-05-15T14:37:50.596339+00:00","id":48828,"links":{},"metadata":{"_buckets":{"deposit":"54bf88eb-e152-4550-b553-cfd60491f292"},"_deposit":{"created_by":1,"id":"48828","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"48828"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00048828","sets":["1"]},"author_link":["491779","491778"],"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2017-07","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"87","bibliographicPageStart":"79","bibliographicVolumeNumber":"40","bibliographic_titles":[{"bibliographic_title":"Physica medica"}]}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Introduction: To develop real-time image processing for image-guided radiotherapy, we evaluated several neural network models for use with different imaging modalities, including X-ray fluoroscopic image denoising.\n\\nMethods & Materials: Setup images of prostate cancer patients were acquired with two oblique X-ray fluoroscopic units. Two types of residual network were designed: a convolutional autoencoder (rCAE) and a convolutional neural network (rCNN). We changed the convolutional kernel size and number of convolutional layers for both networks, and the number of pooling and upsampling layers for rCAE. The ground-truth image was applied to the contrast-limited adaptive histogram equalization (CLAHE) method of image processing. Network models were trained to keep the quality of the output image close to that of the ground-truth image from the input image without image processing. For image denoising evaluation, noisy input images were used for the training.\n\\nResults: More than 6 convolutional layers with convolutional kernels > 5×5 improved image quality. However, this did not allow real-time imaging. After applying a pair of pooling and upsampling layers to both networks, rCAEs with > 3 convolutions each and rCNNs with > 12 convolutions with a pair of pooling and upsampling layers achieved real-time processing at 30 frames per second (fps) with acceptable image quality.\n\\nConclusions: Use of our suggested network achieved real-time image processing for contrast enhancement and image denoising by the use of a conventional modern personal computer.","subitem_description_type":"Abstract"}]},"item_8_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Istituti Editoriali e Poligrafici Internazionali"}]},"item_8_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1016/j.ejmp.2017.07.013","subitem_relation_type_select":"DOI"}}]},"item_8_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1120-1797","subitem_source_identifier_type":"ISSN"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"森, 慎一郎"}],"nameIdentifiers":[{"nameIdentifier":"491778","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"森 慎一郎","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"491779","nameIdentifierScheme":"WEKO"}]}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Deep architecture neural network-based real-time image processing  for image-guided radiotherapy","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Deep architecture neural network-based real-time image processing  for image-guided radiotherapy"}]},"item_type_id":"8","owner":"1","path":["1"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-04-27"},"publish_date":"2018-04-27","publish_status":"0","recid":"48828","relation_version_is_last":true,"title":["Deep architecture neural network-based real-time image processing  for image-guided radiotherapy"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T23:23:52.529656+00:00"}