The cross-sectional area (CSA) of the spinal cord (SC) computed from its segmentation is a relevant clinical biomarker for the diagnosis and monitoring of cord compression and atrophy. One key limitation of existing automatic methods is that their SC segmentations depend on the MRI contrast, resulting in different CSA across contrasts. Furthermore, these methods rely on CNNs, leaving a gap in the literature for exploring the performance of modern deep learning (DL) architectures. In this study, we extend our recent work \citeBdard2023TowardsCS by evaluating the contrast-agnostic SC segmentation capabilities of different classes of DL architectures, namely, ConvNeXt, vision transformers (ViTs), and hierarchical ViTs. We compared 7 different DL models using the open-source \textitSpine Generic Database of healthy participants () consisting of 6 MRI contrasts per participant. Given a fixed dataset size, our results show that CNNs produce robust SC segmentations across contrasts, followed by ConvNeXt, and hierarchical ViTs. This suggests that: (i) inductive biases such as learning hierarchical feature reprensentations via pooling (common in CNNs) are crucial for good performance on SC segmentation, and (ii) hierarchical ViTs that incorporate several CNN-based priors can perform similarly to pure CNN-based models.
@inproceedings{karthik2024contrastagnostic,title={Contrast-agnostic Spinal Cord Segmentation: A Comparative Study of ConvNets and Vision Transformers},author={Karthik, Enamundram Naga and Bedard, Sandrine and Valosek, Jan and Chandar, Sarath and Cohen-Adad, Julien},booktitle={Medical Imaging with Deep Learning},year={2024},url={https://openreview.net/forum?id=n6D25aqdV3},}
2023
arXiv
Towards contrast-agnostic soft segmentation of the spinal cord
Sandrine Bédard*, Naga Karthik Enamundram*, Charidimos Tsagkas, Emanuele Pravatà, Cristina Granziera, Andrew Smith, Kenneth Arnold Weber II, and Julien Cohen-Adad
@misc{bédard2023contrastagnostic,title={Towards contrast-agnostic soft segmentation of the spinal cord},author={Bédard*, Sandrine and Enamundram*, Naga Karthik and Tsagkas, Charidimos and Pravatà, Emanuele and Granziera, Cristina and Smith, Andrew and II, Kenneth Arnold Weber and Cohen-Adad, Julien},year={2023},eprint={2310.15402},archiveprefix={arXiv},primaryclass={eess.IV},note={*shared first authorship}}
IEEE OJEMB
Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines
Enamundram Naga Karthik, Farida Cheriet, and Catherine Laporte
IEEE Open Journal of Engineering in Medicine and Biology, 2023
@article{10086579,author={Naga Karthik, Enamundram and Cheriet, Farida and Laporte, Catherine},journal={IEEE Open Journal of Engineering in Medicine and Biology},title={Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines},year={2023},volume={},number={},pages={1-7},doi={10.1109/OJEMB.2023.3262965},}
2022
MELBA
Label fusion and training methods for reliable representation of inter-rater uncertainty
Andreanne Lemay, Charley Gros, Enamundram Naga Karthik, and Julien Cohen-Adad
@article{melba:2022:031:lemay,title={Label fusion and training methods for reliable representation of inter-rater uncertainty},author={Lemay, Andreanne and Gros, Charley and Naga Karthik, Enamundram and Cohen-Adad, Julien},journal={Machine Learning for Biomedical Imaging},volume={1},issue={January 2023 issue},year={2022},pages={1--27},issn={2766-905X},doi={https://doi.org/10.59275/j.melba.2022-db5c},url={https://melba-journal.org/2022:031},}
MedNeurIPS
Segmentation of Multiple Sclerosis Lesion across Hospitals: Learn Continually or Train from Scratch?
Enamundram Naga Karthik, Anne Kerbrat, Pierre Labauge, Tobias Granberg, Jason Talbott, Daniel S Reich, Massimo Filippi, Rohit Bakshi, Virginie Callot, Sarath Chandar, and Julien Cohen-Adad
MedNeurIPS: Medical Imaging Meets NeurIPS Workshop, 2022
@article{nagakarthik2022Segmentation,title={Segmentation of Multiple Sclerosis Lesion across Hospitals: Learn Continually or Train from Scratch?},author={Naga Karthik, Enamundram and Kerbrat, Anne and Labauge, Pierre and Granberg, Tobias and Talbott, Jason and Reich, Daniel S and Filippi, Massimo and Bakshi, Rohit and Callot, Virginie and Chandar, Sarath and Cohen-Adad, Julien},journal={MedNeurIPS: Medical Imaging Meets NeurIPS Workshop},year={2022},}
2021
SPIE
Three-dimensional segmentation of the scoliotic spine from MRI using unsupervised volume-based MR-CT synthesis
Enamundram M. V. Naga Karthik, Catherine Laporte, and Farida Cheriet
@inproceedings{10.1117/12.2580677,author={Karthik, Enamundram M. V. Naga and Laporte, Catherine and Cheriet, Farida},title={{Three-dimensional segmentation of the scoliotic spine from MRI using unsupervised volume-based MR-CT synthesis}},volume={11596},booktitle={Medical Imaging 2021: Image Processing},editor={Išgum, Ivana and Landman, Bennett A.},organization={International Society for Optics and Photonics},publisher={SPIE},pages={115961H},keywords={Vertebrae segmentation , Scoliosis , Cross-modality synthesis, Volume translation , 3D CycleGAN},year={2021},doi={10.1117/12.2580677},url={https://doi.org/10.1117/12.2580677},}
2020
JASA
Automatic tongue surface extraction from three-dimensional ultrasound vocal tract images
Enamundram M. V. Naga Karthik, Elham Karimi, Steven M. Lulich, and Catherine Laporte
The Journal of the Acoustical Society of America, 2020
@article{doi:10.1121/10.0000891,author={Naga Karthik, Enamundram M. V. and Karimi, Elham and Lulich, Steven M. and Laporte, Catherine},title={Automatic tongue surface extraction from three-dimensional ultrasound vocal tract images},journal={The Journal of the Acoustical Society of America},volume={147},number={3},pages={1623-1633},year={2020},doi={10.1121/10.0000891},url={https://doi.org/10.1121/10.0000891},eprint={https://doi.org/10.1121/10.0000891}}