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This research evaluates the nnU-Net v2 architecture for detecting and segmenting periapical lesions in CBCT images.

April 1, 2026Open Access

Fully automated 3D segmentation of periapical lesions on CBCT images using nnU-Net v2

Key Points

This research evaluates the nnU-Net v2 architecture for detecting and segmenting periapical lesions in CBCT images.
Retrospective analysis of 150 CBCT scans, with 140 annotated for lesions.
Manual segmentation performed by a trained researcher and verified by an endodontist.
nnU-Net v2 model trained on 80% of data and tested on the remaining 10%.
Measurement of segmentation performance using metrics including Dice coefficient and Intersection over Union.
Voxel-level segmentation performance: Dice coefficient (DC) 0.74, IoU 0.60, sensitivity 0.73, precision 0.80.
Lesion-wise detection performance: sensitivity 0.75, precision 0.82, F1-score 0.78.
Model demonstrates moderate-to-good segmentation and good lesion detection performance under clinical conditions.

Abstract

to evaluate the performance of an nnU-Net v2–based architecture for the voxel-level detection, lesion-wise detection and 3D segmentation of periapical lesions on CBCT images. In this retrospective study, 150 CBCT scans acquired for various clinical indications were initially evaluated. 10 scans met the exclusion criteria, leaving 140 CBCT scans that were annotated for the presence of periapical lesions. Periapical lesions were manually segmented on a web-based platform by a trained researcher and verified by an experienced endodontist to obtain the ground-truth segmentations. An nnU-Net v2 model, configured in the 3D full-resolution setting, was trained on 80% of the data, validated on 10%, and tested on the remaining 10%. Segmentation performance was assessed at the voxel-wise using Dice coefficient (DC), Intersection over Union (IoU, Jaccard), 95% Hausdorff distance (HD95), sensitivity (recall), precision metrics. Mean, standard deviation (SD), median and interquartile range (IQR) across the 14 test scans were reported, and 95% confidence intervals (CIs) were estimated using a parametric t-distribution. A lesion-wise detection performance was assessed at the root level using sensitivity, precision and F1-score; results were stratified by lesion volume. On the test set, segmentation performance at voxel-level was DC 0.74 (SD 0.14; 95% CI 0.67–0.81), IoU 0.60 (SD 0.15; 95% CI 0.53–0.68), HD95 12.02 mm (SD 18.85 mm; 95% CI 2.15–21.90 mm), sensitivity 0.73 (SD 0.18; 95% CI 0.63–0.82), precision 0.80 (SD 0.11; 95% CI 0.75–0.86). and AUC was 0.86. Lesion-wise detection performance was sensitivity 0.75; precision 0.82, and the lesion-wise F1-score was 0.78. The nnU-Net v2–based model achieved moderate-to-good three-dimensional segmentation performance and good detection perfomance for periapical lesions on CBCT under routine clinical imaging conditions, with high discriminative ability at the voxel level. In its current form, the model appears suitable as an adjunctive tool to support expert interpretation rather than as a stand-alone decision-making system. Not applicable.

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Öksüzoğlu et al. (Mon,) studied this question.

synapsesocial.com/papers/69ccb69d16edfba7beb884c2 https://doi.org/https://doi.org/10.1186/s12880-026-02316-0

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