XCT-SAM：用于工业XCT缺陷分割的SAM顺序参数高效域适应
XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation
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中文总结 AI 辅助
针对增材制造X射线计算机断层扫描图像缺陷分割难题，提出XCT-SAM框架，先在合金微观结构数据集微调Conv-LoRA适配器，再转移到XCT图像，参数高效且冻结超99%模型，实验表明其性能优于基线，有效实现工业XCT缺陷分割。
中文摘要 AI 辅助
增材制造（AM）X射线计算机断层扫描（XCT）图像中的缺陷分割具有挑战性，因为存在严重的类别不平衡和扫描条件下的大分布偏移。尽管像Segment Anything Model（SAM）这样的基础模型提供了强大的通用分割先验，但它们的自然图像预训练在AM XCT域中转移效果不佳。我们提出了XCT-SAM，一种用于AM XCT缺陷分割的顺序参数高效适应框架。先在合金微观结构数据集上微调Conv-LoRA适配器，再将其转移到XCT图像，逐步弥合域差距。使用秩r = 2的Conv-LoRA适配器，该框架在训练约有415万个参数时，将卷积空间归纳偏差注入SAM主干并保持超99%的模型冻结。我们在分布外的CycleGAN-XCT基准和真实世界的NIST XCT扫描上评估XCT-SAM，它始终优于零样本SAM和其他域适应的SAM基线，取得了最佳的整体IoU和Dice分数。这些结果证明了使用参数高效适配器进行中间域适应对工业XCT缺陷分割的有效性。
英文摘要
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 https://github.com/Mahedi-61/XCT-SAM.git
