Update README.md

README.md

@@ -119,21 +119,20 @@ plt.show()
 If you use this dataset, please acknowledge our lab **[Chehab Lab](https://chehablab.com/)** and cite the original dataset:
 
 ```bibtex
-@article{bashiri2026ms3seg,
-  title={A Multiple Sclerosis MRI Dataset with Tri-Mask Annotations for Lesion Segmentation},
-  author={Bashiri Bawil, Mahdi and Shamsi, Mousa and Ghalehasadi, Aydin and Jafargholkhanloo, Ali Fahmi and Shakeri Bavil, Abolhassan},
-  journal={Scientific Data},
-  year={2026},
-  volume={XX},
-  pages={XXX},
-  doi={10.XXXX/XXXXX},
-  publisher={Nature Publishing Group}
+@article{Bawil2026,
+  author = {Bawil, Mahdi Bashiri and Shamsi, Mousa and Ghalehasadi, Aydin and Jafargholkhanloo, Ali Fahmi and Bavil, Abolhassan Shakeri},
+  title = {A Multiple Sclerosis MRI Dataset with Tri-Mask Annotations for Lesion Segmentation},
+  journal = {Scientific Data},
+  year = {2026},
+  month = apr,
+  day = {14},
+  abstract = {Multiple sclerosis (MS) lesion segmentation from MRI is essential for diagnosis, monitoring, and treatment evaluation, yet existing datasets exhibit limited geographic diversity, scanner vendor bias, and inability to distinguish normal from pathological white matter hyperintensities. We present MS3SEG, a publicly available dataset comprising 100 MS patients from an Iranian cohort acquired on a 1.5 T Toshiba scanner, featuring multi-sequence MRI (T1-weighted, T2-weighted, T2-FLAIR in axial and sagittal planes) with novel tri-mask annotations. Expert annotators systematically delineated three classes on axial T2-FLAIR images: ventricles, normal white matter hyperintensities (age-related or CSF-contaminated regions), and abnormal white matter hyperintensities (MS lesions), incorporating automated quality control to minimize annotation errors. This tri-mask framework addresses a critical clinical challenge where benign periventricular hyperintensities are frequently misclassified as pathological lesions. Data are provided in multiple formats: raw DICOM, preprocessed NIfTI with co-registration and brain extraction, ground-truth masks, and visualization overlays. Baseline validation using U-Net, U-Net++, UNETR, and Swin UNETR across 5-fold cross-validation demonstrated dataset quality, with U-Net achieving Dice coefficients of 0.7469 for binary lesion segmentation and 0.6686 for multi-class abnormal hyperintensity segmentation. MS3SEG enables research on anatomically-aware segmentation, algorithm robustness to real-world clinical acquisition variations, and clinically relevant normal-versus-abnormal hyperintensity distinction, complementing existing 3D volumetric MS datasets.},
+  issn = {2052-4463},
+  doi = {10.1038/s41597-026-07184-5},
+  url = {https://doi.org/10.1038/s41597-026-07184-5}
 }
 ```
 
-**Dataset DOI:** https://doi.org/10.6084/m9.figshare.30393475
-
-
 ## 📜 License
 
 This dataset is released under the **Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication**.