Mr. Damien Dasnoy Profile

Damien Dasnoy

at Univ Catholique de Louvain

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Publications (3)

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Proceedings Article | 15 March 2019 Paper

Semantic segmentation of computed tomography for radiotherapy with deep learning: compensating insufficient annotation quality using contour augmentation

Umair Javaid, Damien Dasnoy, John Lee

Proceedings Volume 10949, 109492P (2019) https://doi.org/10.1117/12.2512461

KEYWORDS: Image segmentation, Bladder, Rectum, Data modeling, Computed tomography, Radiotherapy, Convolution

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In radiotherapy treatment planning, manual annotation of organs-at-risk and target volumes is a difficult and time-consuming task, prone to intra and inter-observer variabilities. Deep learning networks (DLNs) are gaining worldwide attention to automate such annotative tasks because of their ability to capture data hierarchy. However, for better performance DLNs require large number of data samples whereas annotated medical data is scarce. To remedy this, data augmentation is used to increase the training data for DLNs that enables robust learning by incorporating spatial/translational invariance into the training phase. Importantly, performance of DLNs is highly dependent on the ground truth (GT) quality: if manual annotation is not accurate enough, the network cannot learn better than the annotated example. This highlights the need to compensate for possibly insufficient GT quality using augmentation, i.e., by providing more GTs per image, in order to improve performance of DLNs. In this work, small random alterations were applied to GT and each altered GT was considered as an additional annotation. Contour augmentation was used to train a dilated U-Net in multiple GTs per image setting, which was tested on a pelvic CT dataset acquired from 67 patients to segment bladder and rectum in a multi-class segmentation setting. By using contour augmentation (coupled with data augmentation), the network learnt better than with data augmentation only, as it was able to correct slightly offset contours in GT. The segmentation results produced were quantified using spatial overlap, distance-based and probabilistic measures. The Dice score for bladder and rectum are reported as 0.88±0.19 and 0.89±0.04, whereas the average symmetric surface distance are 0.22 ± 0.09 mm and 0.09 ± 0.05 mm, respectively.

Proceedings Article | 8 March 2019 Presentation + Paper

Optimal intermittent measurements for tumor tracking in x-ray guided radiotherapy

Antoine Aspeel, Damien Dasnoy, Raphaël Jungers, Benoît Macq

Proceedings Volume 10951, 109510C (2019) https://doi.org/10.1117/12.2512859

KEYWORDS: Tumors, X-rays, X-ray imaging, Radiotherapy, System identification, Genetic algorithms, Motion models, Optimization (mathematics), Particles

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In radiation therapy, tumor tracking is a challenging task that allows a better dose delivery. One practice is to acquire X-ray images in real-time during treatment, that are used to estimate the tumor location. These informations are used to predict the close future tumor trajectory. Kalman prediction is a classical approach for this task. The main drawback of X-ray acquisition is that it irradiates the patient, including its healthy tissues. In the classical Kalman framework, X-ray measurements are taken regularly, i.e. at a constant rate. In this paper, we propose a new approach which relaxes this constraint in order to take measurements when they are the most useful. Our aim is for a given budget of measurements to optimize the tracking process. This idea naturally brings to an optimal intermittent Kalman predictor for which measurement times are selected to minimize the mean squared prediction error over the complete fraction. This optimization problem can be solved directly when the respiratory model has been identified and the optimal sampling times can be computed at once. These optimal measurement times are obtained by solving a combinatorial optimization problem using a genetic algorithm. We created a test benchmark on trajectories validated on one patient. This new prediction method is compared to the regular Kalman predictor and a relative improvement of 9:8% is observed on the root mean square position estimation error.

Proceedings Article | 17 November 2017 Paper

Design and implementation of a MRI compatible and dynamic phantom simulating the motion of a tumor in the liver under the breathing cycle

Arnould Geelhand de Merxem, Vianney Lechien, Tanguy Thibault, Damien Dasnoy, Benoît Macq

Proceedings Volume 10572, 105720X (2017) https://doi.org/10.1117/12.2285620

KEYWORDS: Magnetic resonance imaging, Tumors, Tissues, Liver, Cameras, Magnetism, Pancreas, Sodium, Stomach, Optical tracking

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In the context of cancer treatment by proton therapy, research is carried out on the use magnetic resonance imaging (MRI) to perform real-time tracking of tumors during irradiation. The purpose of this combination is to reduce the irradiation of healthy tissues surrounding the tumor, while using a non-ionizing imaging method. Therefore, it is necessary to validate the tracking algorithms on real-time MRI sequences by using physical simulators, i.e. a phantom. Our phantom is a device representing a liver with hepatocellular carcinoma, a stomach and a pancreas close to the anatomy and the magnetic properties of the human body, animated by a motion similar to the one induced by the respiration. Many anatomical or mobile phantoms already exist, but the purpose here is to combine a reliable representation of the abdominal organs with the creation and the evaluation of a programmable movement in the same device, which makes it unique. The phantom is composed of surrogate organs made of CAGN gels. These organs are placed in a transparent box filled with water and attached to an elastic membrane. A programmable electro-pneumatic system creates a movement, similarly to a human diaphragm, by inflating and deflating the membrane. The average relaxation times of the synthetic organs belongs to a range corresponding to the human organs values (T1 = [458.7-1660] ms, T2 = [39.3-89.1] ms). The displacement of the tumor is tracked in real time by a camera inside the MRI. The amplitude of the movement varies from 12.8 to 20.1 mm for a periodic and repeatable movement. Irregular breath patterns can be created with a maximum amplitude of 40 mm.

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