Academic Journal
Distortion Correction and Denoising of Light Sheet Fluorescence Images
| العنوان: | Distortion Correction and Denoising of Light Sheet Fluorescence Images |
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| المؤلفون: | Julia, Adrien, Iguernaissi, Rabah, Michel, François, J, Matarazzo, Valéry, Merad, Djamal |
| المساهمون: | Images et Modèles (I&M), Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon) (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Institut de Neurobiologie de la Méditerranée Aix-Marseille Université (INMED - INSERM U1249), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Turing Centre for Living Systems Marseille (TCLS), ANR-16-CONV-0001,CENTURI,CenTuri : Centre Turing des Systèmes vivants(2016) |
| المصدر: | ISSN: 1424-8220 ; Sensors ; https://amu.hal.science/hal-04546787 ; Sensors, 2024, 24 (7), pp.2053. ⟨10.3390/s24072053⟩. |
| بيانات النشر: | HAL CCSD MDPI |
| سنة النشر: | 2024 |
| المجموعة: | Université de Toulon: HAL |
| مصطلحات موضوعية: | light sheet fluorescence microscopy, neuroscience, preprossessing, denoising, deconvolution, axial distortion, deep learning, auto-encoder, [INFO]Computer Science [cs] |
| الوصف: | International audience ; Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson–Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | hal-04546787; https://amu.hal.science/hal-04546787; https://amu.hal.science/hal-04546787/document; https://amu.hal.science/hal-04546787/file/sensors-24-02053-v2.pdf |
| DOI: | 10.3390/s24072053 |
| الاتاحة: | https://amu.hal.science/hal-04546787 https://amu.hal.science/hal-04546787/document https://amu.hal.science/hal-04546787/file/sensors-24-02053-v2.pdf https://doi.org/10.3390/s24072053 |
| Rights: | http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.FB48AF33 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3390/s24072053 |
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