cs.CVSep 7, 2026

ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement

Authors: Sang-Jin Park, Jinyoung Choi, Seokwon Kim, Seungeon Song, Insu Park, Dougho Park, Taeyeon Kim, Youjin Lee, 

Organizations: Pohang Stroke and Spine Hospital, Pohang, Republic of Korea · School of CSEE, Handong Global University, Pohang, Republic of Korea · School of Convergence Science and Technology, Pohang University of Science and Technology, Pohang, Republic of Korea · Department of Neurosurgery, Kyungpook National University Hospital, Kyungpook National University College of Medicine, Daegu, Republic of Korea

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Abstract

Purpose: This study aims to develop an AI framework applicable for postoperative imaging for automated measurement of spinopelvic parameters on radiographs with robustness to the presence of spinal implants. Materials and Methods: We retrospectively reviewed lateral lumbar spine radiographs from two institutions (Internal: January 2017--December 2024; External: October 2021--September 2025). We developed the Restore, Segment, and Measure (RSM) framework, incorporating a novel Artifact Removal Network based on Autoencoding and Inpainting (ARNAI) to mitigate implant-related artifacts in postoperative radiographs. Segmentation and spinopelvic parameter (PT, LL, SS, SCA) measurement performance were assessed using Wilcoxon signed-rank tests and intraclass correlation coefficients. Results: When ARNAI was added to a recent Transformer-based segmentation model, FCBFormer, the mean DSC increased to 0.870 from 0.814, with marked gains at L3--L5 and smaller improvements at L1--L2. On 91 radiographs with implants, the mean L4--L5 segmental Cobb angle error decreased to 4.7 ° from 15.6--16.2 °, an average error reduction of 70%. The ICC for L4--L5 segmental Cobb angle improved to 0.54 (Rater 1) and 0.59 (Rater 2) from 0.18, and ICCs for pelvic tilt, lumbar lordosis, and sacral slope all exceeded 0.70. The improvement in L4--L5 segmental Cobb angle error was statistically significant in the internal implant-containing cohort after correction for multiple comparisons. Conclusion: The proposed RSM framework improved automated spinopelvic parameter measurement in implant-containing postoperative radiographs. By mitigating implant-related artifacts, ARNAI improved segmentation and downstream measurement accuracy, with the greatest benefit observed for L4--L5 segmental Cobb angle estimation, where the mean error was reduced by approximately 70%.

Figures & tables

ModelL1L2L3L4L5SacrumF1F2Mean
Internal test set
FCBFormer [ 8 ]0.8395 (0.2070)0.8673 (0.1966)0.7852 (0.2406)0.6620 (0.1890)0.6807 (0.1950)0.8737 (0.0515)0.9069 (0.0681)0.8932 (0.0806)0.8136 (0.1087)
RSM (Ours)0.8575 (0.1781)0.8860 (0.1610)0.8811 (0.1613)0.8461 (0.1594)0.8170 (0.1771)0.8733 (0.0498)0.9033 (0.0718)0.8935 (0.0785)0.8697 (0.0970)
External test set (Generalization)
FCBFormer [ 8 ]0.7905 (0.2669)0.8480 (0.2471)0.8332 (0.2237)0.6737 (0.1773)0.6661 (0.1897)0.8951 (0.0375)0.8930 (0.0944)0.8735 (0.1331)0.8091 (0.1712)
RSM (Ours)0.7992 (0.2433)0.8642 (0.1996)0.8789 (0.1599)0.8414 (0.1349)0.8109 (0.1441)0.8771 (0.0495)0.8803 (0.0887)0.8650 (0.1345)0.8521 (0.1443)
Table 1: Segmentation performance of RSM in terms of DSC, compared with FCBFormer. Models were trained solely on the implant-free internal trainset and evaluated on both the internal test set and an external test set to demonstrate generalization capability. Values are reported as mean (standard deviation).
Param.Cohort / ModelRelative to Rater 1Relative to Rater 2
Mean (SD)Median (IQR)RICC (95% CI)Mean (SD)Median (IQR)RICC (95% CI)
Internal test set
PTImplant-free1.7 (3.1)1.1 (1.2)0.930.92 (0.88–0.94)1.1 (1.4)0.9 (1.1)0.980.98 (0.97–0.99)
Implant (Baseline)2.3 (3.9)1.2 (1.9)0.870.87 (0.81–0.91)2.4 (4.2)1.3 (1.8)0.860.85 (0.78–0.90)
Implant (RSM)2.0 (3.8)0.9 (1.4)0.880.88 (0.83–0.92)2.3 (3.9)1.2 (1.9)0.870.87 (0.81–0.91)
LLImplant-free4.6 (3.9)3.4 (5.3)0.920.88 (0.72–0.94)5.0 (3.6)4.1 (4.6)0.920.88 (0.64–0.94)
Table 2: Comprehensive statistical evaluation of spinopelvic parameter estimation on the internal test set (143 implant-free images and 91 implant-containing images) and the external test set (88 implant-containing images). Only radiographs accepted by both models were included in the analysis.
Figure 1: An overview of the RSM framework. (1) Mitigation of implant-related artifacts using ARNAI, and (2) Segmentation mask extraction and spinopelvic parameter estimation results.
Figure 2: The procedure of ARNAI. (a) Gamma-corrected original input image. (b) The output of VQ-GAN. (c) The intensity difference between (a) and (b). The implant regions exhibit higher values than other regions. (d) The binary mask of implant regions. (e) The reconstructed image in which the implant regions were removed and reconstructed by the inpainting algorithm.
Figure 3: Qualitative comparison of inpainting methods for implant removal in spine radiographs. The figure presents two different cases (rows). (a) Original radiographs showing implants in the spine. (b) Inpainting results using Stable Diffusion [ 7 ] . (c) Inpainting results using Telea inpainting algorithm. As discussed in the text, Telea inpainting algorithm (c) better preserves the original structure of the bones and introduces fewer artifacts compared to Stable Diffusion Inpainting (b).
Figure 4: Qualitative comparison of segmentation results with and without the proposed ARNAI module on two implant-containing spine radiographs (cases 1 and 2). From left to right: (a) original input image, (b) ground-truth annotation, (c) baseline FCBFormer output (DSC ≈ 0.7651/0.8093), and (d) RSM output (DSC ≈ 0.9265/0.9119). Color overlays denote vertebrae L1–L5, sacrum (S), and bilateral femoral heads F1 and F2. Incorporation of ARNAI mitigates implant-related artifacts and improves anatomical boundary delineation.
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