{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T11:47:35Z","timestamp":1720784855692},"reference-count":80,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,10,16]],"date-time":"2019-10-16T00:00:00Z","timestamp":1571184000000},"content-version":"vor","delay-in-days":107,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["softxjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["SoftwareX"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1016\/j.softx.2019.100347","type":"journal-article","created":{"date-parts":[[2019,10,29]],"date-time":"2019-10-29T11:53:20Z","timestamp":1572350000000},"page":"100347","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"C","title":["Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging"],"prefix":"10.1016","volume":"10","author":[{"given":"Jeremiah W.","family":"Sanders","sequence":"first","affiliation":[]},{"given":"Justin R.","family":"Fletcher","sequence":"additional","affiliation":[]},{"given":"Steven J.","family":"Frank","sequence":"additional","affiliation":[]},{"given":"Ho-Ling","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jason M.","family":"Johnson","sequence":"additional","affiliation":[]},{"given":"Zijian","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Henry Szu-Meng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Aradhana M.","family":"Venkatesan","sequence":"additional","affiliation":[]},{"given":"Rajat J.","family":"Kudchadker","sequence":"additional","affiliation":[]},{"given":"Mark D.","family":"Pagel","sequence":"additional","affiliation":[]},{"given":"Jingfei","family":"Ma","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.softx.2019.100347_b1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.softx.2019.100347_b2","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.softx.2019.100347_b3","series-title":"Grand challenges in biomedical image analysis","author":"van Ginneken","year":"2019"},{"key":"10.1016\/j.softx.2019.100347_b4","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.softx.2019.100347_b5","series-title":"Going deeper with convolutions","author":"Szegedy","year":"2014"},{"key":"10.1016\/j.softx.2019.100347_b6","series-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet","year":"2016"},{"key":"10.1016\/j.softx.2019.100347_b7","series-title":"Deep residual learning for image recognition","author":"He","year":"2015"},{"key":"10.1016\/j.softx.2019.100347_b8","series-title":"Identity mappings in deep residual networks","author":"He","year":"2016"},{"key":"10.1016\/j.softx.2019.100347_b9","series-title":"Aggregated residual transformations for deep neural networks","author":"Xie","year":"2016"},{"key":"10.1016\/j.softx.2019.100347_b10","series-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.softx.2019.100347_b11","series-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"Szegedy","year":"2016"},{"key":"10.1016\/j.softx.2019.100347_b12","series-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"10.1016\/j.softx.2019.100347_b13","series-title":"Densely connected convolutional networks","author":"Huang","year":"2016"},{"key":"10.1016\/j.softx.2019.100347_b14","series-title":"Mobilenetv2: Inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"key":"10.1016\/j.softx.2019.100347_b15","series-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2014"},{"issue":"5","key":"10.1016\/j.softx.2019.100347_b16","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional neural networks for medical image analysis: Full training or fine tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.softx.2019.100347_b17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.softx.2019.100347_b18","series-title":"Proc. 13th int. conf. cont. auto. rob. vis. (ICARCV)","first-page":"844","article-title":"Medical image classification with convolutional neural network","author":"Li","year":"2014"},{"key":"10.1016\/j.softx.2019.100347_b19","series-title":"Breast mass classification from mammograms using deep convolutional neural networks","author":"L\u00e9vy","year":"2016"},{"issue":"6","key":"10.1016\/j.softx.2019.100347_b20","doi-asserted-by":"crossref","first-page":"3888","DOI":"10.1002\/mrm.27677","article-title":"Development and clinical implementation of seednet: A sliding-window convolutional neural network for radioactive seed identification in MRI-assisted radiosurgery (MARS)","volume":"81","author":"Sanders","year":"2019","journal-title":"Magn Reson Med"},{"issue":"4","key":"10.1016\/j.softx.2019.100347_b21","doi-asserted-by":"crossref","first-page":"S80","DOI":"10.1016\/j.brachy.2018.04.136","article-title":"Seednet for automated detection and localization of radioactive seeds in post-implant MRI: A comparison with and without the use of an endorectal coil for imaging","volume":"17","author":"Sanders","year":"2018","journal-title":"Brachytherapy"},{"issue":"1","key":"10.1016\/j.softx.2019.100347_b22","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s12968-018-0471-x","article-title":"Automated cardiovascular magnetic resonance image analysis with fully convolutional networks","volume":"20","author":"Bai","year":"2018","journal-title":"J Cardiovasc Magn Reson"},{"key":"10.1016\/j.softx.2019.100347_b23","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1002\/jmri.26337","article-title":"Fully automatic segmentation on prostate MR images based on cascaded fully convolution network","volume":"49","author":"Zhu","year":"2018","journal-title":"J Magn Reson Imaging"},{"issue":"215","key":"10.1016\/j.softx.2019.100347_b24","article-title":"Deep learning renal segmentation for fully automated radiation dose estimation in unsealed source therapy","volume":"8","author":"Jackson","year":"2018","journal-title":"Front Oncol"},{"key":"10.1016\/j.softx.2019.100347_b25","series-title":"Proc. SPIE int. soc. opt. eng, Vol. 10574","article-title":"Fully convolutional neural networks improve abdominal organ segmentation","author":"Bobo","year":"2018"},{"issue":"8","key":"10.1016\/j.softx.2019.100347_b26","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","article-title":"Automatic multi-organ segmentation on abdominal CT with dense V-networks","volume":"37","author":"Gibson","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"issue":"2049","key":"10.1016\/j.softx.2019.100347_b27","article-title":"Automatic segmentation of kidneys using deep learning for total kidney volume quantification in autosomal dominant polycystic kidney disease","volume":"7","author":"Sharma","year":"2017","journal-title":"Sci Rep"},{"key":"10.1016\/j.softx.2019.100347_b28","series-title":"SUMNet: Fully convolutional model for fast segmentation of anatomical structures in ultrasound volumes","author":"Nandamuri","year":"2019"},{"key":"10.1016\/j.softx.2019.100347_b29","doi-asserted-by":"crossref","first-page":"44635","DOI":"10.1109\/ACCESS.2018.2864592","article-title":"Multichannel fully convolutional network for coronary artery segmentation in X-ray angiograms","volume":"6","author":"Fan","year":"2018","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.softx.2019.100347_b30","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1002\/mp.12763","article-title":"Automated mammographic breast density estimation using a fully convolutional network","volume":"45","author":"Lee","year":"2018","journal-title":"Med Phys"},{"issue":"5","key":"10.1016\/j.softx.2019.100347_b31","doi-asserted-by":"crossref","first-page":"852","DOI":"10.2967\/jnumed.117.198051","article-title":"Zero-echo-time and dixon deep -pseudo-CT (ZeDD CT): Direct generation of pseudo-CT images for pelvic PET\/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI","volume":"59","author":"Leynes","year":"2018","journal-title":"J Nucl Med"},{"issue":"4","key":"10.1016\/j.softx.2019.100347_b32","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1002\/mp.12155","article-title":"MR-based synthetic CT generation using a deep convolutional neural network method","volume":"44","author":"Han","year":"2017","journal-title":"Med Phys"},{"key":"10.1016\/j.softx.2019.100347_b33","series-title":"Male pelvic synthetic CT generation from T1-weighted MRI using 2D and 3D convolutional neural networks","author":"Fu","year":"2018"},{"key":"10.1016\/j.softx.2019.100347_b34","series-title":"Proc. SPIE med. imag.: Biomedical applications in molecular, structural, and functional imaging, Vol. 10953","article-title":"Pseudo-CT image generation from mdixon MRI images using fully convolutional neural networks","author":"Stadelmann","year":"2019"},{"key":"10.1016\/j.softx.2019.100347_b35","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med Image Anal"},{"key":"10.1016\/j.softx.2019.100347_b36","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s40658-018-0225-8","article-title":"A deep learning approach for 18F-FDG PET attenuation correction","volume":"5","author":"Liu","year":"2018","journal-title":"EJNMMI Phys"},{"issue":"12","key":"10.1016\/j.softx.2019.100347_b37","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/aac763","article-title":"Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images","volume":"63","author":"Gong","year":"2018","journal-title":"Phys Med Biol"},{"issue":"2","key":"10.1016\/j.softx.2019.100347_b38","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1148\/radiol.2017170700","article-title":"Deep learning MR imaging\u2013based attenuation correction for PET\/MR imaging","volume":"286","author":"Liu","year":"2017","journal-title":"Radiology"},{"key":"10.1016\/j.softx.2019.100347_b39","series-title":"Deep learning enables automatic detection and segmentation of brain metastases on multi-sequence MRI","author":"Gr\u00f8vik","year":"2019"},{"key":"10.1016\/j.softx.2019.100347_b40","series-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.softx.2019.100347_b41","series-title":"3D U-Net: Learning dense volumetric segmentation from sparse annotation","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"10.1016\/j.softx.2019.100347_b42","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.softx.2019.100347_b43","series-title":"Medgan: Medical image translation using GANs","author":"Armanious","year":"2018"},{"key":"10.1016\/j.softx.2019.100347_b44","article-title":"Inferring PET from MRI with pix2pix","volume":"30","author":"Jung","year":"2018","journal-title":"Benelux Conf. Art. Intell"},{"key":"10.1016\/j.softx.2019.100347_b45","series-title":"Proc. IEEE comput. soc. conf. comput. vis. pattern. recognit","first-page":"9059","article-title":"Generating synthetic x-ray images of a person from the surface geometry","author":"Teixeira","year":"2018"},{"key":"10.1016\/j.softx.2019.100347_b46","series-title":"Few-shot 3D multi-modal medical image segmentation using generative adversarial learning","author":"Mondal","year":"2018"},{"key":"10.1016\/j.softx.2019.100347_b47","series-title":"Conditional adversarial network for semantic segmentation of brain tumor","author":"Rezaei","year":"2017"},{"key":"10.1016\/j.softx.2019.100347_b48","series-title":"Conditional generative refinement adversarial networks for unbalanced medical image semantic segmentation","author":"Rezaei","year":"2018"},{"issue":"1","key":"10.1016\/j.softx.2019.100347_b49","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/TMI.2018.2858752","article-title":"Deep generative adversarial neural networks for compressive sensing MRI","volume":"38","author":"Mardani","year":"2019","journal-title":"IEEE Trans Med Imag"},{"issue":"6","key":"10.1016\/j.softx.2019.100347_b50","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2017.2785879","article-title":"DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction","volume":"37","author":"Yang","year":"2018","journal-title":"IEEE Trans Med Imag"},{"key":"10.1016\/j.softx.2019.100347_b51","series-title":"Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss","author":"Quan","year":"2017"},{"key":"10.1016\/j.softx.2019.100347_b52","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.compmedimag.2018.10.005","article-title":"Image super-resolution using progressive generative adversarial networks for medical image analysis","volume":"71","author":"Mahapatra","year":"2019","journal-title":"Comput Med Imaging Graph"},{"key":"10.1016\/j.softx.2019.100347_b53","series-title":"Brain MRI super-resolution using 3D generative adversarial networks","author":"Sanchez","year":"2018"},{"key":"10.1016\/j.softx.2019.100347_b54","series-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2016"},{"key":"10.1016\/j.softx.2019.100347_b55","series-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu","year":"2017"},{"issue":"1","key":"10.1016\/j.softx.2019.100347_b56","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-based convolutional networks for accurate object detection and segmentation","volume":"38","author":"Girshick","year":"2016","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.softx.2019.100347_b57","series-title":"You only look once: Unified, real-time object detection","author":"Redmon","year":"2015"},{"key":"10.1016\/j.softx.2019.100347_b58","series-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"Ren","year":"2015"},{"key":"10.1016\/j.softx.2019.100347_b59","series-title":"SSD: Single shot multibox detector","author":"Liu","year":"2015"},{"issue":"1","key":"10.1016\/j.softx.2019.100347_b60","first-page":"00","article-title":"Convolutional neural networks for automated fracture detection and localization on wrist radiographs","volume":"1","author":"Thian","year":"2019","journal-title":"Radiol: Artif Intell"},{"issue":"17","key":"10.1016\/j.softx.2019.100347_b61","doi-asserted-by":"crossref","first-page":"5135","DOI":"10.1158\/0008-5472.CAN-18-0494","article-title":"Identification of metastatic lymph nodes in MR imaging with faster region-based convolutional neural networks","volume":"78","author":"Lu","year":"2018","journal-title":"Cancer Res"},{"key":"10.1016\/j.softx.2019.100347_b62","series-title":"Keras: the python deep learning library","author":"Chollet","year":"2015"},{"key":"10.1016\/j.softx.2019.100347_b63","series-title":"TensorFlow: Large-scale machine learning on heterogeneous distributed systems","author":"Abadi","year":"1998"},{"issue":"11","key":"10.1016\/j.softx.2019.100347_b64","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc IEEE"},{"key":"10.1016\/j.softx.2019.100347_b65","series-title":"Deconvolution and checkerboard artifacts","author":"Odena","year":"2016"},{"key":"10.1016\/j.softx.2019.100347_b66","series-title":"Tversky loss function for image segmentation using 3D fully convolutional deep networks","author":"Salehi","year":"2017"},{"key":"10.1016\/j.softx.2019.100347_b67","series-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.softx.2019.100347_b68","series-title":"Proc ISMRM workshop mach. learn.","article-title":"A fully convolutional network utilizing depth-wise separable convolutions for semantic segmentation of anatomy in MRI of the prostate after permanent implant brachytherapy","author":"Sanders","year":"2018"},{"issue":"3","key":"10.1016\/j.softx.2019.100347_b69","doi-asserted-by":"crossref","first-page":"S16","DOI":"10.1016\/j.brachy.2019.04.039","article-title":"Multi-tasking neural networks for anatomy segmentation in prostate brachytherapy MRI","volume":"18","author":"Sanders","year":"2019","journal-title":"Brachytherapy"},{"key":"10.1016\/j.softx.2019.100347_b70","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.brachy.2018.05.003","article-title":"Parallel imaging compressed sensing for accelerated imaging and improved signal-to-noise ratio in MRI-based post-implant dosimetry of prostate brachytherapy","volume":"17","author":"Sanders","year":"2018","journal-title":"Brachytherapy"},{"issue":"4","key":"10.1016\/j.softx.2019.100347_b71","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1093\/neuonc\/nov179","article-title":"Physiologic MRI for assessment of response to therapy and prognosis in glioblastoma","volume":"18","author":"Shiroishi","year":"2015","journal-title":"Neuro-Oncology"},{"issue":"12","key":"10.1016\/j.softx.2019.100347_b72","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1016\/j.acra.2013.09.003","article-title":"Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease","volume":"20","author":"Seeger","year":"2013","journal-title":"Acad Radiol"},{"key":"10.1016\/j.softx.2019.100347_b73","series-title":"Proc. ISMRM annual meeting","article-title":"Synthesizing rCBV maps from DCE-MRI of brain tumors using conditional adversarial networks","author":"Sanders","year":"2019"},{"key":"10.1016\/j.softx.2019.100347_b74","series-title":"Proceedings of the ISMRM workshop on machine learning part II","article-title":"Deep learning application engine (DLAE): end-to-end development and deployment of medical deep learning algorithms","author":"Sanders","year":"2018"},{"issue":"10","key":"10.1016\/j.softx.2019.100347_b75","doi-asserted-by":"crossref","first-page":"5330","DOI":"10.1118\/1.4961984","article-title":"Patient-specific quantification of image quality: An automated method for measuring spatial resolution in clinical CT images","volume":"43","author":"Sanders","year":"2016","journal-title":"Med Phys"},{"issue":"9","key":"10.1016\/j.softx.2019.100347_b76","doi-asserted-by":"crossref","first-page":"4736","DOI":"10.1002\/mp.12438","article-title":"Patient-specific quantification of image quality: An automated technique for measuring the distribution of organ hounsfield units in clinical chest CT images","volume":"44","author":"Abadi","year":"2017","journal-title":"Med Phys"},{"issue":"1","key":"10.1016\/j.softx.2019.100347_b77","article-title":"Automated, patient-specific estimation of regional imparted energy and dose from tube current modulated computed tomography exams across 13 protocols","volume":"4","author":"Sanders","year":"2017","journal-title":"J Med Imaging (Bellingham)"},{"issue":"3","key":"10.1016\/j.softx.2019.100347_b78","article-title":"Estimating detectability index in vivo: development and validation of an automated methodology","volume":"5","author":"Smith","year":"2017","journal-title":"J Med Imaging (Bellingham)"},{"issue":"6","key":"10.1016\/j.softx.2019.100347_b79","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1002\/mp.12172","article-title":"Image noise and dose performance across a clinical population: Patient size adaptation as a metric of CT performance","volume":"44","author":"Ria","year":"2017","journal-title":"Med Phys"},{"key":"10.1016\/j.softx.2019.100347_b80","series-title":"Proc RSNA annual meeting","first-page":"SSJ21","article-title":"EMTIS: Next-generation performance informatics platform for value-based clinical imaging practice","author":"Ding","year":"2018"}],"container-title":["SoftwareX"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352711019302535?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352711019302535?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T14:23:28Z","timestamp":1709303008000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352711019302535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":80,"alternative-id":["S2352711019302535"],"URL":"https:\/\/doi.org\/10.1016\/j.softx.2019.100347","relation":{},"ISSN":["2352-7110"],"issn-type":[{"value":"2352-7110","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging","name":"articletitle","label":"Article Title"},{"value":"SoftwareX","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.softx.2019.100347","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"simple-article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"100347"}}