Fetal Brain Tissue Annotation and Segmentation Challenge

Congenital disorders are one of the leading causes of infant mortality worldwide [1]. Recently, in-utero MRI of the fetal brain has started to emerge as a valuable tool for investigating the neurological development of fetuses with congenital disorders in order to aid in prenatal planning. Moreover, fetal MRI is a powerful tool to portray the complex neurodevelopmental events during human gestation, which remain to be completely characterized.

Automated segmentation and quantification of the highly complex and rapidly changing brain morphology prior to birth in MRI data would improve the diagnostic process, as manual segmentation is both time consuming and subject to human error and inter-rater variability. It is clinically relevant to analyse information such as the shape or volume of the developing brain structures, as many congenital disorders cause subtle changes to these tissue compartments [2]–[5]. Existing growth data is mainly based on normally developing brains [6]–[8], and growth data for many pathologies and congenital disorders is lacking. The automatic segmentation of the developing human brain would be a first step in performing such an analysis.

From a technical standpoint, there are many challenges that an automatic segmentation method of the fetal brain would need to overcome. The physiology and structures of the human brain are constantly growing and developing throughout gestation. In addition, the quality of the images can be poor due to fetal and maternal movement and imaging artefacts [9]. The boundary between tissues is often unclear due to partial volume effects.

Furthermore, structures in a pathological fetal brain can have a different morphology than those in a nonpathological brain. This can make it challenging for an automatic method to recognize what the structures are. The field of fetal MRI has so far been understudied due to challenges in imaging and due to the lack of public, curated, and annotated ground truth data.

The FeTA challenge is an important step in the development of reliable, valid, and reproducible methods of analyzing high resolution reconstructed MR images of the developing fetal brain from gestational week 21-33. Such new algorithms will have the potential to better understand the underlying causes of congenital disorders and ultimately to guide the development of antenatal/postnatal guidelines and clinical risk stratification tools for early interventions, treatments, and care management decisions.

References

[1] “WHO | Causes of child mortality,” WHO. http://www.who.int/gho/child_health/mortality/causes/en/ (accessed Jun. 07, 2020).

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[3] A. Zugazaga Cortazar, C. Martín Martinez, C. Duran Feliubadalo, M. R. Bella Cueto, and L. Serra, “Magnetic resonance imaging in the prenatal diagnosis of neural tube defects,” Insights Imaging, vol. 4, no. 2, pp. 225–237, Mar. 2013, doi: 10.1007/s13244-013-0223-2.

[4] C. Clouchoux et al., “Delayed Cortical Development in Fetuses with Complex Congenital Heart Disease,” Cereb. Cortex, vol. 23, no. 12, pp. 2932–2943, Dec. 2013, doi: 10.1093/cercor/bhs281.

[5] C. K. Rollins et al., “Regional Brain Growth Trajectories in Fetuses with Congenital Heart Disease,” Ann. Neurol., no. n/a, doi: https://doi.org/10.1002/ana.25940.

[6] D. Prayer et al., “MRI of normal fetal brain development,” Eur. J. Radiol., vol. 57, no. 2, pp. 199–216, Feb. 2006, doi: 10.1016/j.ejrad.2005.11.020.

[7] D. A. Jarvis, C. R. Finney, and P. D. Griffiths, “Normative volume measurements of the fetal intra-cranial compartments using 3D volume in utero MR imaging,” Eur. Radiol., vol. 29, no. 7, pp. 3488–3495, Jul. 2019, doi: 10.1007/s00330-018-5938-5.

[8] V. Kyriakopoulou et al., “Normative biometry of the fetal brain using magnetic resonance imaging,” Brain Struct. Funct., vol. 222, no. 5, pp. 2295–2307, 2017, doi: 10.1007/s00429-016-1342-6.

[9] P de Dumast, P Deman, M Khawam, T Yu, S Tourbier, P Hagmann, P Maeder, JP Thiran, R Meuli, V Dunet, M Koob, M Bach Cuadra, “Translating fetal brain magnetic resonance image super-resolution into the clinical environment [abstract],” Marseille, Feb. 2020, vol. 05.