Dr. Nathan Lauzeral Profile

Dr. Nathan Lauzeral

at Hera-MI

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Publications (2)

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Proceedings Article | 3 April 2024 Presentation + Paper

Annotation-free deep-learning framework for microcalcifications detection on mammograms

Paul Terrassin, Mickael Tardy, Nathan Lauzeral, Nicolas Normand

Proceedings Volume 12927, 129270W (2024) https://doi.org/10.1117/12.3008304

KEYWORDS: Cancer detection, Mammography, Education and training, Magnesium, Image segmentation, Neurological disorders, Network architectures, Deep convolutional neural networks, Computer aided detection, Breast cancer

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Breast cancer detection at an early stage significantly increases the chances of recovery for patients. Mammography (MG) is one of the most popular non-invasive and high-resolution imaging allowing radiologists to depict early signs of the disease. Microcalcifications (MCs) often occupy less than 1mm in size and can represent a high risk of suspicion depending on the spatial distribution, morphology, and their evolution over time. Their detection is challenging both the clinicians and computer-aided detection tools. In this work, we propose a novel annotation-free framework designed specifically for the MCs detection and trained in a self-supervised manner thanks to the generation of synthetic MCs. Inspired by the UNet3+ architecture, we reduced its number of parameters to make it applicable in practice and added multi-scale features to enrich fine-grained details with more global context information. Both multi-channel segmentation and multi-class classification tasks are implemented in a multi-scale output approach to catch MC of various sizes. We perform a comparison with several state-of-the-art methods, including different flavors of ResNet-22, ConvNeXt, and UNet3+. An analysis of classification and segmentation performances has been done, using the Gradient-weighted Class Activation Mapping method to make classifiers visually explainable. In this study, we used two public datasets, INBreast and Breast MicroCalcifications Dataset for validation and test purposes. We achieved an AUC score of 0.93 in the characterization of malignant MCs while having a semantic segmentation precision of 0.70. To the best of our knowledge, we are the first study claiming segmentation performances on the BMCD dataset.

Proceedings Article | 4 April 2022 Presentation + Paper

Combining machine learning and artery characterization to identify the main bifurcations in 3D vascular trees

Ibtissam Essadik, Anass Nouri, Nathan Lauzeral, Raja Touahni, Romain Bourcier, Florent Autrusseau

Proceedings Volume 12036, 120360E (2022) https://doi.org/10.1117/12.2605459

KEYWORDS: Arteries, Machine learning, 3D acquisition, Brain, Aneurysms, Image segmentation, 3D image processing

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The cerebral vascular system is constituted by all the arteries and veins irrigating the brain. This vascular tree starts from two pairs of arteries, the vertebral arteries and the internal carotid arteries. These latter divide into a circular shape being called the Circle of Willis (CoW). There is considerable variability in the structure of the CoW among patients. The CoW can host various vascular diseases, among which intracranial aneurysms are of particular importance because their occurrence, or more precisely their rupture, can be devastating. Intracranial aneurysms often occur at the bifurcations of the arterial tree (saccular aneurysms), as a bulge in the vessel wall. It is crucial to recognize and monitor such aneurysms. Anatomical identification of the bifurcations of the CoW can be of great help to establish a diagnosis or to plan a surgical operation. In this study, we propose an automatic solution to categorize the vascular anatomy of the CoW in 3D volumes by identifying its main constituting bifurcations. Our solution combines machine learning and a multivariate analysis (Linear Discriminant Analysis: LDA). The LDA works as a classifier and reduces the dimensionality of the dataset by transforming the selected features in a lower dimensional space. This work is a preliminary study prior to moving to human cerebrovascular images. We evaluate the proposed method using several machine learning techniques combined with a leave-one-out validation applied on a set of 30 synthetic vascular images as well as 30 mouse cerebral vasculatures.

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