CT image segmentation of bone for medical additive manufacturing using a convolutional neural network
Computers in Biology and Medicine , Volume 103 p. 130- 139
Background: The most tedious and time-consuming task in medical additive manufacturing (AM) is image segmentation. The aim of the present study was to develop and train a convolutional neural network (CNN) for bone segmentation in computed tomography (CT) scans.
Method: The CNN was trained with CT scans acquired using six different scanners. Standard tessellation language (STL) models of 20 patients who had previously undergone craniotomy and cranioplasty using additively manufactured skull implants served as “gold standard” models during CNN training. The CNN segmented all patient CT scans using a leave-2-out scheme. All segmented CT scans were converted into STL models and geometrically compared with the gold standard STL models.
Results: The CT scans segmented using the CNN demonstrated a large overlap with the gold standard segmentation and resulted in a mean Dice similarity coefficient of 0.92 ± 0.04. The CNN-based STL models demonstrated mean surface deviations ranging between −0.19 mm ± 0.86 mm and 1.22 mm ± 1.75 mm, when compared to the gold standard STL models. No major differences were observed between the mean deviations of the CNN-based STL models acquired using six different CT scanners.
Conclusions: The fully-automated CNN was able to accurately segment the skull. CNNs thus offer the opportunity of removing the current prohibitive barriers of time and effort during CT image segmentation, making patient-specific AM constructs more accesible.
|Additive manufacturing, Artificial intelligence, Computed tomography (CT), Convolutional neural network, Image segmentation|
|Computers in Biology and Medicine|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam, The Netherlands|
Minnema, J, van Eijnatten, M.A.J.M, Kouw, W.M, Diblen, F, Mendrik, A, & Wolff, J. (2018). CT image segmentation of bone for medical additive manufacturing using a convolutional neural network. Computers in Biology and Medicine, 103, 130–139. doi:10.1016/j.compbiomed.2018.10.012