Abstract
Accurate segmentation of the spine in computed tomography (CT) images is mandatory for quantitative analysis, e.g. in osteoporosis, but remains challenging due to high variability in vertebral morphology and spinal anatomy among patients. Conventionally, spine segmentation was performed by model-based techniques employing spine atlases or statistical shape models. We argue that such approaches, even though intuitive, fail to address clinical abnormalities such as vertebral fractures, scoliosis, etc. We propose a novel deep learning-based method for segmenting the spine, which does not rely on any pre-defined shape model. We employ two networks: one for localisation and another for segmentation. Since a typical spine CT scan cannot be processed at once owing to its large dimensions, we find that both nets are essential to work towards a perfect segmentation. We evaluate our framework on three datasets containing healthy and fractured cases: two private and one public. Our approach achieves a mean Dice coefficient of \({\sim }0.87\), which is comparable but not higher than the state-of-art model-based approaches. However, we show that our approach handles degenerate cases more accurately.
A. Sekuboyina and J. Kukačka—Contributed equally.
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Notes
- 1.
A cascaded fusion of these nets was also tried where the patches for the segmentation net are obtained only from the region proposed by the attention map. We observed that the accuracy of this approach was not superior to our approach of late-fusion.
- 2.
We would like to thank Klinder et al., the authors and our industry partners (Philips, Hamburg, Germany), for providing us with the segmentation of Datasets 1 and 2 based on their approach in [1].
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Acknowledgements
This work was funded from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (GA637164–iBack–ERC-2014-STG).
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Sekuboyina, A., Kukačka, J., Kirschke, J.S., Menze, B.H., Valentinitsch, A. (2018). Attention-Driven Deep Learning for Pathological Spine Segmentation. In: Glocker, B., Yao, J., Vrtovec, T., Frangi, A., Zheng, G. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science(), vol 10734. Springer, Cham. https://doi.org/10.1007/978-3-319-74113-0_10
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