According to the 2023 update, liver disease causes two million deaths per year and accounts for 4% of all global deaths. Chronic liver disease (CLD) and cirrhosis are the tenth leading cause of death in the United States. ***Early detection of cirrhosis*** allows for timely interventions to limit disease progression. To address this, ***we collected and manually segmented 310 T1 and 318 T2 abdomen MRI images*** from different stages of cirrhosis, and we presented them to the community for use in further AI research. **The Cirrhotic Liver dataset** provides a valuable resource for advancing clinical research in automated liver cirrhosis analysis. This dataset reflects real-world complexities by including scans that exhibit various morphological alterations. These alterations include contour nodularity, hepatic segment atrophy or hypertrophy, and other changes associated with cirrhosis complications, such as ascites, varices, and splenomegaly. All these variations contribute to the dataset's variability and complexity. Such complexity is crucial for training robust and generalizable deep learning models that perform effectively on unseen data. The dataset becomes more representative of actual clinical scenarios by incorporating a wide range of disease presentations. **Data and MRI acquisition**: We selected and prepared the specific protocol for MRI data acquisition. (1) We collected MRI scans from three different scanners to maintain heterogeneity. MRI scans were obtained from the Achieva, Philips (1.5T and 3T) and Symphony, Siemens 1.5T scanners with full anonymization protocol. Most (over 95%) of T1W images were in the post-contrast portal venous phase for better organ contrast. (2) To ensure image quality, participating radiologists selected and annotated ‘good enough’ images; the rest were excluded from the study. Some MRI scans have mild to moderate artifacts (like motion or susceptibility). We excluded the images with poor image quality and significant motion artifacts. (3) This study included patients with liver cirrhosis and the non-cirrhotic control group (healthy folder). We aimed to capture comprehensive liver cirrhosis examples with different etiologies and stages, emphasizing the variability and diverse complications. **Metadata Descriptions** (1) *CirrMRI600+_CompleteData_age_gender_evaluation:* Information on age, gender, and radiological evaluation for all 337 patients. (2) *T1_age_gender_evaluation:* Age, gender, and radiological evaluation for the 310 patients with T1-weighted MRI images. (3) *T2_age_gender_evaluation:* Age, gender, and radiological evaluation for the 318 patients with T2-weighted MRI images. (4) *T1&T2_Paired_age_gender_evaluation:* Age, gender, and radiological evaluation for the 291 patients with both T1 and T2-weighted MRI images. (5) *Healthy_demographics:* Age and gender information for 55 individuals from the control group. (6) *Labels*: Label Descriptions