Automatic bone segmentation for oral surgical planning / Automatikus csontszegmentáció szájsebészeti mutéti tervezés támogatására

Introduction: Evolvement of digital technology encouraged the use of virtual dental and dental surgical planning and navigated surgery. The variety of cases is significant, therefore even for an expert, it is challanging to recover a traumatic case, or to design and apply implants with high anatomical precision. Objective: High-quality image acquisition and processing are essential to the process. CT (computer tomography) or CBCT (cone-beam computer tomography) images having bad initial quality greatly decrease the performance of the processing algorithms and affect the quality of the reconstructed 3D models. 3D reconstruction using manual segmentation takes several hours of work and expertise, which significantly increases the overall cost and time of 3D CAD/CAM (computer-aided design/computer-aided manufacturing) based surgical planning and production processes. Material and method: In this paper, we introduce a bone segmentation algorithm specifically designed for surgical planning, and evaluate its benefits for the aforementioned goal. The approach relies on edge detection, mathematical morphology and basic image-processing operations. Results: Performance has been evaluated on 40 series of manual segmentation. Results (precision 90%) show that the algorithm is fast and accurate so it is applicable for surgical planning. Conclusion: Our algorithm significantly reduces the execution time of segmentation and surgical planning, therefore increases the efficiency of the overall procedure.

Two Classes of T1 Hypointense Lesions in Multiple Sclerosis With Different Clinical Relevance.

Background: Hypointense lesions on T1-weighted images have important clinical relevance in multiple sclerosis patients. Traditionally, spin-echo (SE) sequences are used to assess these lesions (termed black holes), but Fast Spoiled Gradient-Echo (FSPGR) sequences provide an excellent alternative. Objective: To determine whether the contrast difference between T1 hypointense lesions and the surrounding normal white matter is similar on the two sequences, whether different lesion types could be identified, and whether the clinical relevance of these lesions types are different. Methods: Seventy-nine multiple sclerosis patients' lesions were manually segmented, then registered to T1 sequences. Median intensity values of lesions were identified on all sequences, then K-means clustering was applied to assess whether distinct clusters of lesions can be defined based on intensity values on SE, FSPGR, and FLAIR sequences. The standardized intensity of the lesions in each cluster was compared to the intensity of the normal appearing white matter in order to see if lesions stand out from the white matter on a given sequence. Results: 100% of lesions on FSPGR images and 69% on SE sequence in cluster #1 exceeded a standardized lesion distance of Z = 2.3 (p < 0.05). In cluster #2, 78.7% of lesions on FSPGR and only 17.7% of lesions on SE sequence were above this cutoff value, meaning that these lesions were not easily seen on SE images. Lesion count in the second cluster (lesions less identifiable on SE) significantly correlated with the Expanded Disability Status Scale (EDSS) (R: 0.30, p ≤ 0.006) and with disease duration (R: 0.33, p ≤ 0.002). Conclusion: We showed that black holes can be separated into two distinct clusters based on their intensity values on various sequences, only one of which is related to clinical parameters. This emphasizes the joint role of FSPGR and SE sequences in the monitoring of MS patients and provides insight into the role of black holes in MS.

Macro- and microstructural alterations of the subcortical structures in episodic cluster headache.

Background Previous functional and structural imaging studies have revealed that subcortical structures play a key a role in pain processing. The recurring painful episodes might trigger maladaptive plasticity or alternatively degenerative processes that might be detected by MRI as changes in size or microstructure. In the current investigation, we aimed to identify the macro- and microstructural alterations of the subcortical structures in episodic cluster headache. Methods High-resolution T1-weighted and diffusion-weighted MRI images with 60 gradient directions were acquired from 22 patients with cluster headache and 94 healthy controls. Surface-based segmentation analysis was used to measure the volume of the subcortical nuclei, and mean diffusion parameters (fractional anisotropy, mean, radial and axial diffusivity) were determined for these structures. In order to understand whether the size and diffusion parameters could be investigated in a headache lateralised manner, first the asymmetry of the size and diffusion parameters of the subcortical structures was analysed. Volumes and diffusion parameters were compared between groups and correlated with the cumulative number of headache days. To account for the different size of the patient and control group, a bootstrap approach was used to investigate the stability of the findings. Results A significant lateralisation of the size (caudate, putamen and thalamus) and the diffusion parameters of the subcortical structures were found in normal controls. In cluster headache patients, the mean fractional anisotropy of the right amygdalae, the mean axial and mean diffusivity of the right caudate nucleus and the radial diffusivity of the right pallidum were higher. The mean anisotropy of the right pallidum was lower in patients. Conclusion The analysis of the pathology in the subcortical structures in episodic cluster headache reveals important features of the disease, which might allow a deeper insight into the pathomechanism of the pain processing in this headache condition.