DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning

Lumbar disc localization and labeling with a probabilistic model on both pixel and object features

Jason J Corso, Alomari Raja’S, and Vipin Chaudhary · 2008

Earlier work this paper cites.

An automated vertebra identification and segmentation in CT images

Melih S Aslan, Asem Ali, Ham Rara, and Aly A Farag · 2010

Earlier work this paper cites.

Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults

Richard A Deyo, Sohail K Mirza, Brook I Martin, William Kreuter, David C Goodman, and Jeffrey G Jarvik · 2010

Earlier work this paper cites.

Indirect costs associated with surgery for low back pain-a secondary analysis of clinical trial data

Reginald Fayssoux, Neil I Goldfarb, Alexander R Vaccaro, and James Harrop · 2010

Earlier work this paper cites.

Computer-aided diagnosis of lumbar stenosis conditions

Soontharee Koompairojn, Kathleen Hua, Kien A Hua, and Jintavaree Srisomboon · 2010

Earlier work this paper cites.

Labeling of lumbar discs using both pixel-and object-level features with a two-level probabilistic model

Alomari Raja’S, Jason J Corso, and Vipin Chaudhary · 2011

Earlier work this paper cites.

A new approach to automatic disc localization in clinical lumbar MRI: combining machine learning with heuristics

Subarna Ghosh, Manavender R Malgireddy, Vipin Chaudhary, and Gurmeet Dhillon · 2012

Earlier work this paper cites.

Adadelta: an adaptive learning rate method

Original

Matthew D Zeiler · 2012

Earlier work this paper cites.

Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF

Ayse Betul Oktay and Yusuf Sinan Akgul · 2013

Cited alongside, same era.

Interrater and intrarater agreements of magnetic resonance imaging findings in the lumbar spine: significant variability across degenerative conditions

Michael C Fu, Rafael A Buerba, William D Long, Daniel J Blizzard, Andrew W Lischuk, Andrew H Haims, and Jonathan N Grauer · 2014

Cited alongside, same era.

Vertebrae detection and labelling in lumbar MR images

Meelis Lootus, Timor Kadir, and Andrew Zisserman · 2014

Cited alongside, same era.

U-net: Convolutional networks for biomedical image segmentation

Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015

Cited alongside, same era.

Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs

Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al · 2016

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM van der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, et al · 2017

Later among the works it cites.

Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application

Jan Egger, Christopher Nimsky, and Xiaojun Chen · 2017

Later among the works it cites.

Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017

Later among the works it cites.

Issls prize in bioengineering science 2017: automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist

Amir Jamaludin, Meelis Lootus, Timor Kadir, Andrew Zisserman, Jill Urban, Michele C Battié, Jeremy Fairbank, Iain McCall, Genodisc Consortium, et al · 2017

Later among the works it cites.