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GENTEL : GENerating Training data Efficiently for Learning to segment medical images

Abstract : Accurately segmenting MRI images is crucial for many clinical applications. However, manually segmenting images with accurate pixel precision is a tedious and time consuming task. In this paper we present a simple, yet effective method to improve the efficiency of the image segmentation process. We propose to transform the image annotation task into a binary choice task. We start by using classical image processing algorithms with different parameter values to generate multiple, different segmentation masks for each input MRI image. Then, the user, instead of segmenting the pixels of the images, she/he only needs to decide if a segmentation is acceptable or not. This method allows us to efficiently obtain high quality segmentations with minor human intervention. With the selected segmentations we train a state-of-the-art neural network model. For the evaluation, we use a second MRI dataset (1.5T Dataset), acquired with a different protocol and containing annotations. We show that the trained network i) is capable to automatically segment cases where none of the classical methods obtained a high quality result ii) generalizes to the second MRI dataset, which was acquired with a different protocol and never seen at training time ; and iii) allows to detect miss-annotations in this second dataset. Quantitatively, the trained network obtains very good results : DICE score - mean 0.98, median 0.99- and Hausdorff distance (in pixels) - mean 4.7, median 2.0-.
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Submitted on : Wednesday, November 4, 2020 - 5:00:26 PM
Last modification on : Wednesday, November 3, 2021 - 8:25:59 AM
Long-term archiving on: : Friday, February 5, 2021 - 7:21:24 PM


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  • HAL Id : hal-02988367, version 1



Rajat Thankur, Sergi Pujades, Lavika Goel, Rolf Pohmann, Jürgen Machann, et al.. GENTEL : GENerating Training data Efficiently for Learning to segment medical images. RFIAP 2020 - Congrés Reconnaissance des Formes, Image, Apprentissage et Perception, Jun 2020, Vannes, France. pp.1-7. ⟨hal-02988367⟩



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