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Deep Learning, Medical Imaging

Measuring Anatomical Variations between Radiotherapy Treatment Sessions to Improve Dose Conformity

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June 3, 2020

- Deep Learning, Medical Imaging

For prostate cancer patients, large organ deformations occurring between radiotherapy treatment sessions create uncertainty about the dose delivered to the tumor and surrounding healthy organs. Segmenting those regions on cone beam CT (CBCT) scans acquired on treatment day would reduce such uncertainties. In our latest work, a 3D U-net deep-learning architecture was trained to segment bladder, rectum and prostate on CBCT scans. Due to the scarcity of contoured CBCT scans, the training set was augmented with CT scans already contoured in the current clinical workflow. Our network was then tested on CBCT scans. The segmentation accuracy (as measured by the Dice similarity coefficient) increased significantly with the number of CBCT and CT scans in the training set, reaching a performance 10% better than conventional approaches based on deformable image registration between planning CT and treatment CBCT scans. Interestingly, adding CT scans to the CBCT training set allowed maintaining high performance, while halving the number of CBCT scans. Hence, our work showed that although CBCT scans included artifacts, cross-domain augmentation of the training set was effective and could rely on large datasets available for planning CT scans.

Collaborations
CHU-UCL-Namur, Belgium
CHU-Charleroi Hôpital André Vésale, Belgium

Links
Publisher: https://www.mdpi.com/2076-3417/10/3/1154
Github: https://github.com/eliottbrion/pelvis_segmentation

In this use case, the prostate is irradiated while minimizing the dose delivered to the healthy bladder and rectum. The image shows an example of automated bladder, rectum and prostate tracking on the CBCT scan acquired before each treatment cession. On this image, each column corresponds to a slice of the same CBCT scan. Dark colors represent the segmentation drawn manually by a human expert, while light colors show our algorithm segmentation. The automated bladder, in pink, rectum, in light green and prostate, in light blue, are close to human expert delineations. A further step will be to highlight these imporvements by showing better tumor coverage and reduction in the doses delivered to the healthy organs that it allows.

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Deep Learning, Medical Imaging

Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)

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February 13, 2020

- Deep Learning, Medical Imaging

TCLearn is a scalable method enabling multiple partners to participate together in distributed learning. Each new iteration of the model is first validated through federated byzantine agreement to guarantee the quality of the resulting model before being recorded in a blockchain. TCLearn preserves the privacy of both the data and the model while ensuring its optimal efficiency.

Link: 10.1109/ACCESS.2019.2959220

Full abstract

Distributed learning across coalitions is becoming popular for multi-centric implementation of deep learning models. However, the level of trust between the members of a coalition can vary and requires different security architectures. Privacy of the training data has been largely described in distributed learning. In this paper, we present a scalable security architecture providing additional features such as validation on the sources quality, confidentiality on the model within a trusted coalition or confidentiality among untrusted partners inside the coalition. More specifically, we propose solutions that guarantee preservation not only of data privacy but also of data quality, enforce a trustworthy sequence of iterative learning, and that lead to equitable sharing of the learned model among the coalition’s members. We give an example of its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.

Distributed learning across coalitions is becoming popular for multi-centric implementation of deep learning models. However, the level of trust between the members of a coalition can vary and requires different security architectures. Privacy of the training data has been largely described in distributed learning. In this paper, we present a scalable security architecture providing additional features such as validation on the sources quality, confidentiality on the model within a trusted coalition or confidentiality among untrusted partners inside the coalition. More specifically, we propose solutions that guarantee preservation not only of data privacy but also of data quality, enforce a trustworthy sequence of iterative learning, and that lead to equitable sharing of the learned model among the coalition’s members. We give an example of its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.

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