Tag: segmentation

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Deep learning–based segmentation of the trigeminal nerve and surrounding vasculature in trigeminal neuralgia

J Neurosurg 143:83–91, 2025

This study developed and validated deep learning U-Net models for automated 3D segmentation of the trigeminal nerve and surrounding vasculature in MRI of trigeminal neuralgia patients, enabling objective quantification of neurovascular conflict features and potentially improving preoperative evaluation and treatment planning.

• Deep learning (U-Net) models were used to segment the trigeminal nerve and surrounding vasculature in patients with trigeminal neuralgia using high-resolution CISS MRI.

• Six U-Net variants with different encoder backbones were tested; SE-ResNet50 performed best overall (Dice score = 0.775, IoU = 0.681).

• The models quantified anatomical features such as the surface area of neurovascular contact and distance to the contact point, showing no significant difference from manual segmentations.

• The best model achieved 100% sensitivity and specificity in detecting neurovascular conflict in the testing set.

• Automated 3D segmentation allows for objective, quantitative evaluation, improving on subjective and time-intensive manual methods.

• Limitations include inability to distinguish vessel type (artery vs. vein) and data from a single institution; future work should address these.

• The method may help standardize neurovascular conflict assessment and improve treatment selection for trigeminal neuralgia.

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Using Novel Segmentation Technology to Define Safe Corridors for Minimally Invasive Posterior Lumbar Interbody Fusion

Operative Neurosurgery 27:14–22, 2024

There has been a rise in minimally invasive methods to access the intervertebral disk space posteriorly given their decreased tissue destruction, lower blood loss, and earlier return to work. Two such options include the percutaneous lumbar interbody fusion through the Kambin triangle and the endoscopic transfacet approach. However, without accurate preoperative visualization, these approaches carry risks of damaging surrounding structures, especially the nerve roots. Using novel segmentation technology, our goal was to analyze the anatomic borders and relative sizes of the safe triangle, trans-Kambin, and the transfacet corridors to assist surgeons in planning a safe approach and determining cannula diameters.

METHODS: The areas of the safe triangle, Kambin, and transfacet corridors were measured using commercially available software (BrainLab, Munich, Germany). For each approach, the exiting nerve root, traversing nerve roots, theca, disk, and vertebrae were manually segmented on 3-dimensional T2-SPACE magnetic resonance imaging using a region-growing algorithm. The triangles’ borders were delineated ensuring no overlap between the area and the nerves.

RESULTS: A total of 11 patients (65.4 ± 12.5 years, 33.3% female) were retrospectively reviewed. The Kambin, safe, and transfacet corridors were measured bilaterally at the operative level. The mean area (124.1 ± 19.7 mm 2 vs 83.0 ± 11.7 mm2 vs 49.5 ± 11.4 mm2 ) and maximum permissible cannula diameter (9.9 ± 0.7 mm vs 6.8 ± 0.5 mm vs 6.05 ± 0.7 mm) for the transfacet triangles were significantly larger than Kambin and the traditional safe triangles, respectively (P < .001).

CONCLUSION: We identified, in 3-dimensional, the borders for the transfacet corridor: the traversing nerve root extending inferiorly until the caudal pedicle, the theca medially, and the exiting nerve root superiorly. These results illustrate the utility of preoperatively segmenting anatomic landmarks, specifically the nerve roots, to help guide decision-making when selecting the optimal operative approach.

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An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

European Spine Journal (2022) 31:2156–2164

Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset.

Methods A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers.

Results The median Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network, although occasional failures were noted. Cross-sectional area and fat fraction of the muscles were in agreement with published data.

Conclusions The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in an accurate and fully automated manner, although it is not without limitations. The model is therefore a suitable research tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

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Intraoperative 3D ultrasound–guided resection of diffuse low-grade gliomas

J Neurosurg 132:518–529, 2020

Extent of resection (EOR) and residual tumor volume are linked to prognosis in low-grade glioma (LGG) and there are various methods for facilitating safe maximal resection in such patients. In this prospective study the authors assess radiological and clinical results in consecutive patients with LGG treated with 3D ultrasound (US)–guided resection under general anesthesia.

METHODS Consecutive LGGs undergoing primary surgery guided with 3D US between 2008 and 2015 were included. All LGGs were classified according to the WHO 2016 classification system. Pre- and postoperative volumetric assessments were performed, and volumetric results were linked to overall and malignant-free survival. Pre- and postoperative health-related quality of life (HRQoL) was evaluated.

RESULTS Forty-seven consecutive patients were included. Twenty LGGs (43%) were isocitrate dehydrogenase (IDH)– mutated, 7 (14%) were IDH wild-type, 19 (40%) had both IDH mutation and 1p/19q codeletion, and 1 had IDH mutation and inconclusive 1p/19q status. Median resection grade was 93.4%, with gross-total resection achieved in 14 patients (30%). An additional 24 patients (51%) had small tumor remnants < 10 ml. A more conspicuous tumor border (p = 0.02) and lower University of California San Francisco prognostic score (p = 0.01) were associated with less remnant tumor tissue, and overall survival was significantly better with remnants < 10 ml (p = 0.03). HRQoL was maintained or improved in 86% of patients at 1 month. In both cases with severe permanent deficits, relevant ischemia was present on diffusionweighted postoperative MRI.

CONCLUSIONS Three-dimensional US–guided LGG resections under general anesthesia are safe and HRQoL is preserved in most patients. Effectiveness in terms of EOR appears to be consistent with published studies using other advanced neurosurgical tools. Avoiding intraoperative vascular injury is a key factor for achieving good functional outcome.

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