Publications

2024

1. MIDL Short

   Contrast-agnostic Spinal Cord Segmentation: A Comparative Study of ConvNets and Vision Transformers

   Enamundram Naga Karthik, Sandrine Bedard, Jan Valosek, Sarath Chandar, and Julien Cohen-Adad

   In Medical Imaging with Deep Learning, 2024

   Abstract Bib PDF Code

   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

1. 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

   Abstract Bib PDF Code

   Uncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks’ behavior. In unsupervised learning tasks, where expert labels are unavailable, it becomes ever more important to critique the model through uncertainties. This paper presents a proof-of-concept for generalizing the aleatoric and epistemic uncertainties in unsupervised MR-CT synthesis of scoliotic spines. A novel adaptation of the cycle-consistency constraint in CycleGAN is proposed such that the model predicts the aleatoric uncertainty maps in addition to the standard volume-to-volume translation between Magnetic Resonance (MR) and Computed Tomography (CT) data. Ablation experiments were performed to understand uncertainty estimation as an implicit regularizer and a measure of the model’s confidence. The aleatoric uncertainty helps in distinguishing between the bone and soft-tissue regions in CT and MR data during translation, while the epistemic uncertainty provides interpretable information to the user for downstream tasks.

   @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 = {https://doi.org/10.1109/OJEMB.2023.3262965},
   }

2022

1. MELBA

   

   Label fusion and training methods for reliable representation of inter-rater uncertainty

   Andreanne Lemay, Charley Gros, Enamundram Naga Karthik, and Julien Cohen-Adad

   Machine Learning for Biomedical Imaging, 2022

   Abstract Bib PDF

   Medical tasks are prone to inter-rater variability due to multiple factors such as image quality, professional experience and training, or guideline clarity. Training deep learning networks with annotations from multiple raters is a common practice that mitigates the model’s bias towards a single expert. Reliable models generating calibrated outputs and reflecting the inter-rater disagreement are key to the integration of artificial intelligence in clinical practice. Various methods exist to take into account different expert labels. We focus on comparing three label fusion methods: STAPLE, average of the rater’s segmentation, and random sampling of each rater’s segmentation during training. Each label fusion method is studied using both the conventional training framework and the recently published SoftSeg framework that limits information loss by treating the segmentation task as a regression. Our results, across 10 data splittings on two public datasets (spinal cord gray matter challenge, and multiple sclerosis brain lesion segmentation), indicate that SoftSeg models, regardless of the ground truth fusion method, had better calibration and preservation of the inter-rater rater variability compared with their conventional counterparts without impacting the segmentation performance. Conventional models, i.e., trained with a Dice loss, with binary inputs, and sigmoid/softmax final activate, were overconfident and underestimated the uncertainty associated with inter-rater variability. Conversely, fusing labels by averaging with the SoftSeg framework led to underconfident outputs and overestimation of the rater disagreement. In terms of segmentation performance, the best label fusion method was different for the two datasets studied, indicating this parameter might be task-dependent. However, SoftSeg had segmentation performance systematically superior or equal to the conventionally trained models and had the best calibration and preservation of the inter-rater variability. SoftSeg has a low computational cost and performed similarly in terms of uncertainty to ensembles which require multiple models and forward passes. Our code is available at https://ivadomed.org.

   @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},
   }

2. 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

   Bib PDF Code

   @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

1. 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

   In Medical Imaging 2021: Image Processing, 2021

   Bib PDF

   @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

1. 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

   Bib

   @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}
   }