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Abstract #2092

Performance of automated segmentation models for the delineation of paediatric craniopharyngiomas.

Annemarie K Knill1,2, Jai Sidpra3, Vitor Nagai Yamaki4,5, Bruno Santanna Peres6, Valentina Lind7, Noor ul Owase Jeelani4, Darren R Hargrave8, Kristian Aquilina4, Ulrike Löbel9, Kshitij Mankad9, and Enrico De Vita1,2
1MR Physics Group. Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom, 2University College London Great Ormond Street Institute of Child Health, London, United Kingdom, 3Developmental Biology and Cancer Section, University College London Great Ormond Street Institute of Child Health, London, United Kingdom, 4Department of Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom, 5Department of Neurosurgery, Hospital das Clinicas sa Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil, 6Department of Radiology, Hospital das Clinicas sa Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil, 7Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, United Kingdom, 8Department of Neuro-oncology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom, 9Department of Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom

Synopsis

Keywords: Neuro, Tumors, AI

Motivation: Accurate automated segmentation of paediatric craniopharyngiomas could improve and standardise measurements of tumour size used to assess response.

Goal(s): To assess the performance of published, freely available model architectures for automated whole-tumour segmentation of paediatric craniopharyngiomas on routine brain MRI.

Approach: Ground-truth segmentations were compared to predicted masks performed with 3 different models (1 DeepMedic and 2 nnU-Net based), using measurements of the Dice coefficient and percentage error in tumour volume.

Results: For whole-tumour segmentation of paediatric craniopharyngioma, the highest Dice scores alongside accurate volume measurements were achieved with models published by Ruffle et.al.(2023).

Impact: Whole-tumour segmentations using models published by Ruffle et.al.(2023) have the potential to aid with volume measurements of paediatric craniopharyngioma on routine brain MRI. Clinical applications would benefit from separate segmentations of solid and cystic tumour in future analysis.

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Abstract

Keywords