Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI
By K. Laukamp, F. Thiele, G. Shakirin, D. Zopfs, A. Faymonville, M. Timmer, D. Maintz, M. Perkuhn, J. Borggrefe. 2018
171 citations recorded by the source corpus.
ObjectivesMagnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations.MethodsWe included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE.ResultsThe DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE.ConclusionsThe DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity.Key Points• Deep learning allows for accurate meningioma detection and segmentation• Deep learning helps clinicians to assess patients with meningiomas• Meningioma monitoring and treatment planning can be improved
Subfields: Epidemiology, Genetics, Pathology and Forensic Medicine
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Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “Meningioma [279] Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric”
- Introduction to artificial intelligence in medicine (2019, result): “…a training set of 249 preoperative MRI glioma cases and segmented different tumour classes as defined by the brain tumour image segmentation benchmark (BRATS benchmark), the program was able to detect 55 of 56 gliomas accurately as compared with results from manual readings by two radiologists [9].”