REPRODUCIBILITY OF TUMOR SEGMENTATION OUTCOMES WITH A DEEP LEARNING MODEL - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

REPRODUCIBILITY OF TUMOR SEGMENTATION OUTCOMES WITH A DEEP LEARNING MODEL

Abstract

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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Dates and versions

hal-04006057 , version 1 (20-03-2023)

Identifiers

  • HAL Id : hal-04006057 , version 1

Cite

Morgane Des Ligneris, Axel Bonnet, Yohan Chatelain, Tristan Glatard, Michaël Sdika, et al.. REPRODUCIBILITY OF TUMOR SEGMENTATION OUTCOMES WITH A DEEP LEARNING MODEL. International Symposium on Biomedical Imaging (ISBI), Apr 2023, Cartagena de Indias, Colombia. ⟨hal-04006057⟩
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