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Spectral augmentation for heart chambers segmentation on conventional contrasted and unenhanced CT scans: an in-depth study

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Recently, machine learning has outperformed established tools for automated segmentation in medical imaging. However, segmentation of cardiac chambers still proves challenging due to the variety of contrast agent injection protocols used in clinical practice, inducing disparities of contrast between cavities. Hence, training a generalist network requires large training datasets representative of these protocols. Furthermore, segmentation on unenhanced CT scans is further hindered by the challenge of obtaining ground truths from these images. Newly available spectral CT scanners allow innovative image reconstructions such as virtual non-contrast (VNC) imaging, mimicking non-contrasted conventional CT studies from a contrasted scan. Recent publications have demonstrated that networks can be trained using VNC to segment contrasted and unenhanced conventional CT scans to reduce annotated data requirements and the need for annotations on unenhanced scans. We propose an extensive evaluation of this statement.

Method

We undertake multiple trainings of a 3D multi-label heart segmentation network with (HU-VNC) and without (HUonly) VNC as augmentation, using decreasing training dataset sizes (114, 76, 57, 38, 29, 19 patients). At each step, both networks are tested on a multi-vendor, multi-centric dataset of 122 patients, including different protocols: pulmonary embolism (PE), chest-abdomen-pelvis (CAP), heart CT angiography (CTA) and true non-contrast scans (TNC). An in-depth comparison of resulting Dice coefficients and distance metrics is performed for the networks trained on the largest dataset.

Results

HU-VNC-trained on 57 patients significantly outperforms HUonly trained on 114 regarding CAP and TNC scans (mean Dice coefficients of 0.881/0.835 and 0.882/0.416, respectively). When trained on the largest dataset, significant improvements in all labels are noted for TNC and CAP scans (mean Dice coefficient of 0.882/0.416 and 0.891/0.835, respectively).

Conclusion

Adding VNC images as training augmentation allows the network to perform on unenhanced scans and improves segmentations on other imaging protocols, while using a reduced training dataset.

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Code availability

Philips IntelliSpace Portal 11.1 and 3D-Slicer 4.10.2 were used to create segmentation ground truths.

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Funding

This work was partially funded by a grant from the Association Nationale de la Recherche et de la Technologie (#2018/1716).

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Authors and Affiliations

  1. Philips Research France, Suresnes, France

    Pierre-Jean Lartaud, Anna Sesilia Vlachomitrou, Jean-Michel Rouet & Olivier Nempont

  2. CREATIS UMR5220, INSERM U1044, INSA, Université de Lyon, Lyon, France

    Pierre-Jean Lartaud, Riham Dessouky, Philippe Douek & Loïc Boussel

  3. Hospices Civils de Lyon, Lyon, France

    David Hallé, Arnaud Schleef, Philippe Douek & Loïc Boussel

Authors
  1. Pierre-Jean Lartaud
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  2. David Hallé
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  3. Arnaud Schleef
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  4. Riham Dessouky
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  5. Anna Sesilia Vlachomitrou
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  9. Loïc Boussel
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Contributions

Authors D. Hallé and A. Schleef contributed equally to this work.

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Correspondence to Pierre-Jean Lartaud.

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Lartaud, PJ., Hallé, D., Schleef, A. et al. Spectral augmentation for heart chambers segmentation on conventional contrasted and unenhanced CT scans: an in-depth study. Int J CARS 16, 1699–1709 (2021). https://doi.org/10.1007/s11548-021-02468-0

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