Reproducibility of Tumor Segmentation Outcomes with a Deep Learning Model

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Des Ligneris, Morgane | Bonnet, Axel | Chatelain, Yohan | Glatard, Tristan | Sdika, Michaël | Vila, Gaël | Wargnier-Dauchelle, Valentine | Camarasu-Pop, Sorina | Frindel, Carole

Edité par CCSD -

International audience. In the last few years, there has been a growing awareness of reproducibility concerns in many areas of science. In this work, our goal is to evaluate the reproducibility of tumor segmentation outcomes produced with a deep segmentation model when MRI images are pre-processed (i) with two different versions of the same pre-processing pipeline, and (ii) by introducing numerical perturbations that mimic executions on different environments. Results show that these two variability sources can lead to important variations of segmentation outcomes: Dice can go as low as 0.59 and Hausdorff distance as high as 84.75. Moreover, both cases show a similar range of values, suggesting that the underlying causes for instability may be numerical stability. This work can be used as a benchmark to improve the numerical stability of the pipeline.

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