Longitudinal Pre- and Post-Treatment Liver CT Dataset for Image Registration

This dataset comprises 128 contrast-enhanced abdominal CT scans from 64 longitudinal studies of patients diagnosed with liver metastases. Each patient underwent paired imaging before and after systemic chemotherapy, enabling quantitative analysis of treatment response and metastatic progression. All scans were acquired in the portal venous phase using a SIEMENS SOMATOM Scope scanner, with in-plane resolution ranging from 0.5–1.0 mm and slice thickness of 1–1.2 mm. For each subject, two imaging timepoints (timepoint-1 and timepoint-2) are provided. At each timepoint the following files are available:• CT.nii — preprocessed contrast-enhanced CT volume (NIfTI format) • LiverMask.nii — expert-validated liver segmentation Segmentations were generated using a hybrid pipeline combining deep learning–based segmentation, expert correction, and final radiologist validation to ensure anatomical accuracy and clinical reliability. Standardized preprocessing steps included isotropic resampling, windowing (center 40 HU, width 450 HU), and intensity normalization to [0,1]. This dataset provides a unique resource for developing and evaluating methods in liver segmentation, deformable image registration, and longitudinal tumor tracking. **Ethics statement:** All procedures involving human participants were approved by the Ethics Committee of Al-Zahra Hospital, Isfahan University of Medical Sciences (IR.MUI.DHMT.REC.1402.054). Written informed consent was waived due to the retrospective nature of the study and complete anonymization of data. **Funding:** This work was supported by the Vice-Chancellery for Research and Technology, Isfahan University of Medical Sciences (Grant No. 3402504). **License:** This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, allowing unrestricted use, distribution, and reproduction with appropriate citation. **Citation:** Zahra Valipour, Hossein Rabbani, Alireza Vard, Mohammadsaleh Jafarpishe, Evaluation of Liver Metastasis Volume Changes in Longitudinal CT images Using Statistical Modeling and Adversarial Image Registration Networks,Results in Engineering,2025,108734,ISSN 2590-1230,https://doi.org/10.1016/j.rineng.2025.108734. and Valipour, Z., Golabchi, M., Rasti, S., Rabbani, H., Vard, A. & Jafarpishe, M.. (2025). *A Longitudinal Abdominal CT Dataset with Expert-Validated Liver and Metastasis Segmentations.* .....