Automatic Size And Pose Homogenization With Spatial Transformer Network To Improve And Accelerate Pediatric Segmentation
| Autor: | Pietro Gori, Alessandro Delmonte, Jeanne Goulin, Sabine Sarnacki, Laurence Rouet, Haithem Boussaid, Bruno Belucci, Isabelle Bloch, Giammarco La Barbera |
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| Přispěvatelé: | Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Images, Données, Signal (IDS), Télécom ParisTech, Institut Polytechnique de Paris (IP Paris), Philips Research, CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Learning, Fuzzy and Intelligent systems (LFI), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), La Barbera, Giammarco, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP) |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
STN [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] Normalization (statistics) Computer Science - Machine Learning kidney Computer Science - Artificial Intelligence Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition [INFO.INFO-IM] Computer Science [cs]/Medical Imaging Machine Learning (stat.ML) 02 engineering and technology [INFO] Computer Science [cs] Machine Learning (cs.LG) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine Statistics - Machine Learning Minimum bounding box Region of interest FOS: Electrical engineering electronic engineering information engineering [INFO.INFO-IM]Computer Science [cs]/Medical Imaging 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] Segmentation Transformer (machine learning model) business.industry Deep learning Image and Video Processing (eess.IV) segmentation [MATH.MATH-AG] Mathematics [math]/Algebraic Geometry [math.AG] Pattern recognition Image segmentation Electrical Engineering and Systems Science - Image and Video Processing Artificial Intelligence (cs.AI) pediatric 020201 artificial intelligence & image processing renal tumor [MATH.MATH-AG]Mathematics [math]/Algebraic Geometry [math.AG] Artificial intelligence Scale (map) business pose size normalization data augmentation |
| Zdroj: | ISBI ISBI 2021-IEEE International Symposium on Biomedical Imaging ISBI 2021-IEEE International Symposium on Biomedical Imaging, Apr 2021, Nice, France. pp.1773-1776, ⟨10.1109/ISBI48211.2021.9434090⟩ |
| DOI: | 10.1109/isbi48211.2021.9434090 |
| Popis: | Due to a high heterogeneity in pose and size and to a limited number of available data, segmentation of pediatric images is challenging for deep learning methods. In this work, we propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN). Our architecture is composed of three sequential modules that are estimated together during training: (i) a regression module to estimate a similarity matrix to normalize the input image to a reference one; (ii) a differentiable module to find the region of interest to segment; (iii) a segmentation module, based on the popular UNet architecture, to delineate the object. Unlike the original UNet, which strives to learn a complex mapping, including pose and scale variations, from a finite training dataset, our segmentation module learns a simpler mapping focusing on images with normalized pose and size. Furthermore, the use of an automatic bounding box detection through STN allows saving time and especially memory, while keeping similar performance. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners. Results indicate that the estimated STN homogenization of size and pose accelerates the segmentation (25h), compared to standard data-augmentation (33h), while obtaining a similar quality for the kidney (88.01\% of Dice score) and improving the renal tumor delineation (from 85.52\% to 87.12\%). Comment: ISBI 2021 |
| Databáze: | OpenAIRE |
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