Conference Proceedings

@inproceedings{Desai2020b,

title = {A Report on the International Workshop on Osteoarthritis Imaging Segmentation Challenge: A Multi-Institute Evaluation on a Standard Dataset},

author = {Arjun D. Desai and Francesco Caliva and Claudia Iriondo and Naji Khosravan and Aliasghar Mortazi and Sachin Jambawalikar and Drew Torigian and Jutta Ellerman and Mehmet Akçakaya and Ulas Bagci and Radhika Tibrewala and Io Flament and Matt O'Brien and Sharmila Majumdar and Mathias Perslev and Akshay Pai and Christian Igel and Erik B. Dam and Sibaji Gaj and Mingrui Yang and Kunio Nakamura and Xiaojuan Li and Cem M. Deniz and Vladimir Juras and Ravinder Regatte and Garry E. Gold and Brian A. Hargreaves and Valentina Pedoia and Akshay S. Chaudhari},

url = {https://index.mirasmart.com/ISMRM2020/PDFfiles/1147.html},

year  = {2020},

date = {2020-08-10},

booktitle = {Proceedings 28th Scientific Meeting, ISMRM},

pages = {1147},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kopanoglu2020b,

title = {Simultaneous multi-slice imaging reduces sensitivity of local-SAR to patient motion at 7T},

author = {Emre Kopanoglu and Cem M. Deniz and Richard G. Wise},

url = {https://index.mirasmart.com/ISMRM2020/PDFfiles/3691.html},

year  = {2020},

date = {2020-08-10},

booktitle = {Proceedings 28th Scientific Meeting, ISMRM},

pages = {3691},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zhang2020,

title = {Attention-based CNN for KL Grade Classification: Data from the Osteoarthritis Initiative},

author = {Bofei Zhang and Jimin Tan and Kyunghyun Cho and Gregory Chang and Cem M. Deniz},

doi = {10.1109/ISBI45749.2020.9098456},

issn = {1945-8452},

year  = {2020},

date = {2020-05-22},

booktitle = {2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},

pages = {731-735},

abstract = {Knee osteoarthritis (OA) is a chronic degenerative disorder of joints and it is the most common reason leading to total knee joint replacement. Diagnosis of OA involves subjective judgment on symptoms, medical history, and radiographic readings using Kellgren-Lawrence grade (KL-grade). Deep learning-based methods such as Convolution Neural Networks (CNN) have recently been applied to automatically diagnose radiographic knee OA. In this study, we applied Residual Neural Network (ResNet) to first detect knee joint from radiographs and later combine ResNet with Convolutional Block Attention Module (CBAM) to make a prediction of the KL-grade automatically. The proposed model achieved a multi-class average accuracy of 74.81%, mean squared error of 0.36, and quadratic Kappa score of 0.88, which demonstrates a significant improvement over the published results. The attention maps were analyzed to provide insights on the decision process of the proposed model. Code is available at https://github.com/denizlab/OAI-KL-Grade-Classification.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Desai2020,

title = {A multi-institute automated segmentation evaluation on a standard dataset: Findings from the international workshop on osteoarthritis imaging segmentation challenge},

author = {A. Desai and F. Caliva and C. Iriondo and N. Khosravan and A. Mortazi and S. Jambawalikar and D. Torigian and J. Ellermann and M. Akcakaya and U. Bagci and R. Tibrewala and I. Flament and M. O'Brian and S. Majumdar and M. Perslev and A. Pai and C. Igel and E. Dam and S. Gaj and M. Yang and K. Nakamura and X. Li and Cem M. Deniz and Vladimir Juras and 

R. Regatte and G. Gold and B. Hargreaves and V. Pedoia and A. Chaudhari},

url = {http://www.sciencedirect.com/science/article/pii/S1063458420305446},

doi = {10.1016/j.joca.2020.02.477},

issn = {1063-4584},

year  = {2020},

date = {2020-04-20},

booktitle = {Osteoarthritis and Cartilage},

volume = {28},

pages = {S304 - S305},

note = {Abstracts from the 2020 OARSI World Congress on Osteoarthritis},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tan2020,

title = {Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation},

author = {Jimin Tan and Bofei Zhang and Kyunghyun Cho and Gregory Chang and Cem M. Deniz},

editor = {Horst K. Hahn and Maciej A. Mazurowski},

doi = {10.1117/12.2551357},

year  = {2020},

date = {2020-03-16},

booktitle = {Medical Imaging 2020: Computer-Aided Diagnosis},

volume = {11314},

pages = {153-161},

publisher = {SPIE},

organization = {International Society for Optics and Photonics},

abstract = {Osteoarthritis (OA) is a chronic degenerative disorder of joints and is the most common reason leading to total knee joint replacement (TKR). In this paper, we implemented a semi-supervised learning approach based on Unsupervised Data Augmentation (UDA) along with valid perturbations for radiographs to enhance the performance of supervised TKR outcome prediction model. Our results suggest that the use of semi-supervised approach provides superior results compared to the supervised approach (AUC of 0.79 ± 0.04 vs 0.74 ± 0.04).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Juras2019,

title = {A novel radiological texture analysis technique for the assessment of cartilage regeneration following GelrinC treatment},

author = {Vladimir Juras and Pavol Szomolanyi and Cem M Deniz and Siegfried Trattnig},

year  = {2019},

date = {2019-07-10},

booktitle = {Proceedings 15th World Congress, International Cartilage Regeneration & Joint Preservation Society, Vancouver, Canada},

pages = {18.4.9},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wang2019b,

title = {Total Knee Replacement Prediction Using Structural MRIs and 3D Convolutional Neural Networks},

author = {Tianyu Wang and Kevin Leung and Kyunghyun Cho and Gregory Chang and Cem M Deniz},

url = {https://openreview.net/pdf?id=B1lw7vS0tN},

year  = {2019},

date = {2019-07-08},

booktitle = {Proceedings of International Conference on Medical Imaging with Deep Learning, London},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wang2019,

title = {3D Convolutional Networks to predict Total Knee Replacement using Structural MRI},

author = {Tianyu Wang and Kevin Leung and Kyunghyun Cho and Gregory Chang and Cem M Deniz},

url = {https://index.mirasmart.com/ISMRM2019/PDFfiles/4880.html},

year  = {2019},

date = {2019-05-11},

booktitle = {Proceedings 27th Scientific Meeting, ISMRM, Montreal},

pages = {4880},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kopanoglu2019,

title = {Implications of within-scan patient head motion on B1+ homogeneity and Specific Absorption Rate at 7T},

author = {Emre Kopanoglu and Alix Jean Deeley Plumley and M. Arcan Erturk and Cem M Deniz and Richard G. Wise},

url = {https://index.mirasmart.com/ISMRM2019/PDFfiles/4686.html},

year  = {2019},

date = {2019-05-11},

booktitle = {Proceedings 27th Scientific Meeting, ISMRM, Montreal},

pages = {4686},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Gyftopoulos2019,

title = {MRI segmentation of the glenoid and humeral head using deep convolutional neural networks},

author = {Soterios Gyftopoulos and Tatiane Rodrigues and Cem M Deniz and J. Dublin and Natalia Gorelik},

doi = {10.1007/s00256-018-3131-1},

year  = {2019},

date = {2019-03-10},

booktitle = {Skeletal Radiol},

volume = {48},

pages = {479},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}