Deep Learning for the Automatic Segmentation of Extracranial Venous Malformations of the Head and Neck from MR Images Using 3D U-Net

Abstract

Background: It is difficult to characterize extracranial venous malformations (VMs) of the head and neck region from magnetic resonance imaging (MRI) manually and one at a time. We attempted to perform the automatic segmentation of lesions from MRI of extracranial VMs using a convolutional neural network as a deep learning tool. Methods: T2-weighted MRI from 53 patients with extracranial VMs in the head and neck region was used for annotations. Preprocessing management was performed before training. Three-dimensional U-Net was used as a segmentation model. Dice similarity coefficients were evaluated along with other indicators. Results: Dice similarity coefficients in 3D U-Net were found to be 99.75% in the training set and 60.62% in the test set. The models showed overfitting, which can be resolved with a larger number of objects, i.e., MRI VM images. Conclusions: Our pilot study showed sufficient potential for the automatic segmentation of extracranial VMs through deep learning using MR images from VM patients. The overfitting phenomenon observed will be resolved with a larger number of MRI VM images.

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

“…73 In contrast, Korte et al investigated the delineations of the parotid gland, submandibular gland, and neck lymph nodes by using a T2WI dataset with multiple 3D-U-Net systems, resulting in DSC values of approximately 0.8. 74 The segmentation of other lesions and anatomical structures such as vestibular schwannoma in the cerebellopontine angle, 75,76 the inner ear and its related structures (e.g., cochlea, vestibule), [77][78][79] and venous malformations of the neck 80 has been described. Segmentation of the inner ear and its related structures necessary for the diagnosis of endolymphatic hydrops has also been a concern (see the section below titled 'Disease classification and diagnosis') and would be valuable for clinical practice.…”

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

“…Similarly, AI has the potential to improve the accuracy and efficiency of venous malformation diagnosis and treatment planning. In a study by Ryu et al (2022), a DL-based method called 3D U-Net was used for the automatic segmentation of extracranial venous malformations in the head and neck region from MRI images [ 170 ]. The method was able to accurately identify and segment venous malformations with a high degree of accuracy, producing a Dice coefficient of 0.87.…”

New Insights into Cervicofacial Vascular Anomalies

“…Considering the impact on patients’ quality of life through aesthetic and functional aspects, the study of these lesions becomes imperative for specialists dealing with head and neck pathology. The complex anatomy of the cervicofacial region adds additional difficulty to the understanding of vascular malformations and tumors, considering the richness of the vascularization of these anatomical regions, with numerous possibilities for the development of collateral blood circulation [ 1 ].…”