Dr. Elodie Lugez Profile

Dr. Elodie Lugez

at Toronto Metropolitan Univ

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Publications (2)

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Proceedings Article | 29 March 2024 Poster + Paper

Using attention-based convolutional auto-encoders for catheter path reconstruction in ultrasound images

Shreyasi Mandal, Srinjoy Bhuiya, Elodie Lugez

Proceedings Volume 12928, 129281N (2024) https://doi.org/10.1117/12.3003897

KEYWORDS: Image segmentation, Reconstruction algorithms, Ultrasonography, 3D modeling, Education and training, Prostate, High dynamic range imaging, 3D image processing, Visual process modeling, Medical image reconstruction, Deep learning

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Purpose: In high-dose-rate prostate brachytherapy, multiple catheters are inserted into the prostate gland. Conventional catheter detection in ultrasound images is hindered by images of a low signal-to-noise ratio, making it difficult to identify the catheters manually. To address this issue, this paper presents an innovative automated deep-learning approach for catheter path reconstruction. Method: A lightweight spatial attention-based autoencoder convolutional neural network is developed to accurately and rapidly segment catheters in real-time volumetric transrectal ultrasound images. To overcome the challenges of noisy and limited annotated data, an auto-augmentation technique is employed, which leverages a controller network to learn the optimal augmentation policy. A 3D random sample consensus-based catheter path reconstruction algorithm is also proposed to transform the catheter segmentations into smooth curves representing their paths. Results: By integrating automated data augmentation with an optimized autoencoder network, structured dropout, and batch normalization techniques, the proposed algorithm successfully detected 98% of the catheter paths tested. The mean tip errors were recorded as 0.18±0.12 mm, while the mean shaft errors were recorded as 0.39±0.28 mm, varying depending on the complexity of the catheter curve path. Notably, the proposed algorithm outperformed existing methods by exhibiting faster inference times, with an average inference time of 0.0029 seconds. Conclusion: The proposed methodology offers a comprehensive approach to enhance the accuracy of catheter path reconstruction in prostate brachytherapy. This lightweight neural network also has the potential to significantly improve the prostate brachytherapy workflow by making the catheter reconstruction process timeefficient.

Proceedings Article | 18 March 2015 Paper

Nonholonomic catheter path reconstruction using electromagnetic tracking

Elodie Lugez, Hossein Sadjadi, Selim Akl, Gabor Fichtinger

Proceedings Volume 9415, 94150L (2015) https://doi.org/10.1117/12.2081561

KEYWORDS: Sensors, Electromagnetism, Motion models, Process modeling, Manufacturing, Filtering (signal processing), Electronic filtering, Error analysis, Motion measurement, Transmitters

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Catheter path reconstruction is a necessary step in many clinical procedures, such as cardiovascular interventions and high-dose-rate brachytherapy. To overcome limitations of standard imaging modalities, electromagnetic tracking has been employed to reconstruct catheter paths. However, tracking errors pose a challenge in accurate path reconstructions. We address this challenge by means of a filtering technique incorporating the electromagnetic measurements with the nonholonomic motion constraints of the sensor inside a catheter. The nonholonomic motion model of the sensor within the catheter and the electromagnetic measurement data were integrated using an extended Kalman filter. The performance of our proposed approach was experimentally evaluated using the Ascension’s 3D Guidance trakStar electromagnetic tracker. Sensor measurements were recorded during insertions of an electromagnetic sensor (model 55) along ten predefined ground truth paths. Our method was implemented in MATLAB and applied to the measurement data. Our reconstruction results were compared to raw measurements as well as filtered measurements provided by the manufacturer. The mean of the root-mean-square (RMS) errors along the ten paths was 3.7 mm for the raw measurements, and 3.3 mm with manufacturer’s filters. Our approach effectively reduced the mean RMS error to 2.7 mm. Compared to other filtering methods, our approach successfully improved the path reconstruction accuracy by exploiting the sensor’s nonholonomic motion constraints in its formulation. Our approach seems promising for a variety of clinical procedures involving reconstruction of a catheter path.

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