XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation
Addressing the challenge of defect segmentation in additive manufacturing XCT images, the proposed XCT-SAM framework sequentially adapts SAM using Conv-LoRA adapters, first on an alloy microstructure dataset then on XCT images, outperforming baselines on CycleGAN-XCT benchmarks and real NIST scans.
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[Submitted on 15 Jul 2026]
Title:XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation
View a PDF of the paper titled XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation, by Md Mahedi Hasan and 5 other authors
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Abstract:Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across scan conditions. Although recent foundation models such as the Segment Anything Model (SAM) provide strong general-purpose segmentation priors, their natural-image pre-training transfers poorly to the AM XCT domain, where defects appear as subtle non-semantic microstructural anomalies. Moreover, adapting SAM to the AM domain is further limited by the large domain gap and scarcity of labeled real XCT data. We present XCT-SAM, a sequential parameter-efficient adaptation framework for AM XCT defect segmentation. Instead of adapting SAM directly from natural images to XCT data, we first fine-tune Conv-LoRA adapters on an alloy-microstructure dataset and subsequently transfer the adapted model to XCT images, progressively bridging the domain gap. Using Conv-LoRA adapters with rank r=2, the framework injects convolutional spatial inductive bias into SAM's backbone while training approximately 4.15M parameters and keeping over 99% of the model frozen. We evaluate XCT-SAM on out-of-distribution CycleGAN-XCT benchmarks and real-world NIST XCT scans. Across both settings, XCT-SAM consistently outperforms zero-shot SAM and other domain-adapted SAM baselines, achieving the best overall IoU and Dice scores. These results demonstrate the effectiveness of intermediate domain adaptation with parameter-efficient adapters for industrial XCT defect segmentation. The source code is publicly available at this https URL
Comments: 10 pages, 7 figures, 3 tables. Accepted to the IAPR Workshop on Machine Vision for Industrial Inspection (MVI2), in conjunction with ICPR 2026. Code: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
ACM classes: I.4.6; I.2.10; I.4.8
Cite as: arXiv:2607.14287 [cs.CV]
(or arXiv:2607.14287v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.14287
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Md Mushfiqur Rahaman [view email] [v1] Wed, 15 Jul 2026 18:49:09 UTC (6,820 KB)
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