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04-06-2019 | Original Article | Issue 7/2019

International Journal of Computer Assisted Radiology and Surgery 7/2019

EasyLabels: weak labels for scene segmentation in laparoscopic videos

Journal:
International Journal of Computer Assisted Radiology and Surgery > Issue 7/2019
Authors:
Félix Fuentes-Hurtado, Abdolrahim Kadkhodamohammadi, Evangello Flouty, Santiago Barbarisi, Imanol Luengo, Danail Stoyanov
Important notes

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s11548-019-02003-2) contains supplementary material, which is available to authorized users.

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Abstract

Purpose

We present a different approach for annotating laparoscopic images for segmentation in a weak fashion and experimentally prove that its accuracy when trained with partial cross-entropy is close to that obtained with fully supervised approaches.

Methods

We propose an approach that relies on weak annotations provided as stripes over the different objects in the image and partial cross-entropy as the loss function of a fully convolutional neural network to obtain a dense pixel-level prediction map.

Results

We validate our method on three different datasets, providing qualitative results for all of them and quantitative results for two of them. The experiments show that our approach is able to obtain at least \(90\%\) of the accuracy obtained with fully supervised methods for all the tested datasets, while requiring \(\sim 13\)\(\times \) less time to create the annotations compared to full supervision.

Conclusions

With this work, we demonstrate that laparoscopic data can be segmented using very few annotated data while maintaining levels of accuracy comparable to those obtained with full supervision.

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Supplementary Material
Supplementary material 1 (mp4 3360 KB)
11548_2019_2003_MOESM1_ESM.mp4
Supplementary material 2 (mp4 97132 KB)
11548_2019_2003_MOESM2_ESM.mp4
Supplementary material 3 (pdf 46 KB)
11548_2019_2003_MOESM3_ESM.pdf
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