## 5 Conclusions  
This study presents a ready-to-use framework for the development of thalamus-segmentation methods, comprising a processed multimodal dataset, code for reproducing the baseline model, and a test set for benchmarking. The dataset, based on the HCP data, encompasses a total of 1063 processed subjects with diffusion data, T1-weighted images, segmentation masks from FSL, FreeSurfer, QuickNAT, and a combination of the aforementioned masks through the STAPLE method. Furthermore, the dataset includes a subset with professionally segmented images. For benchmarking purposes, researchers can submit their predictions to a leaderboard made available on our Codalab platform. A complete framework for training a CNN model is also available on our GitHub for other researchers to build upon. Our proposed method combines T1-weighted images with diffusion MRI, representing a valid option for accurate segmentation of the thalamic structure. We demonstrated that the addition of diffusion MRI yields improvements in thalamus segmentation. Comparisons with existing state-of-the-art methods indicate the superiority of our approach, although a more fair comparison would demand the fine-tuning of QuickNAT using our manual masks. The automatic generation of silver-standard masks circumvented the lack of a large volume of manually annotated data. The model, pre-trained on the silver-standard mask and fine-tuned on a small quantity of manual masks, led to an absolute improvement of 0.122 in the Dice coefficient. **Supplementary Materials:** The file describing the thalamus manual segmentation protocol can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13095284/s1](https://www.mdpi.com/article/10.3390/app13095284/s1)]([https://www.mdpi.com/article/10.3390/app13095284/s1](https://www.mdpi.com/article/10.3390/app13095284/s1)). The dataset, code, environment, and instructions for the benchmark leaderboard can be found at [[https://github.com/MICLab-Unicamp/thalamus_benchmark_diffusion](https://github.com/MICLab-Unicamp/thalamus_benchmark_diffusion)]([https://github.com/MICLab-Unicamp/thalamus_benchmark_diffusion](https://github.com/MICLab-Unicamp/thalamus_benchmark_diffusion)) and [[https://codalab.lisen.upsaclay.fr/competitions/8329](https://codalab.lisen.upsaclay.fr/competitions/8329)]([https://codalab.lisen.upsaclay.fr/competitions/8329](https://codalab.lisen.upsaclay.fr/competitions/8329)) (accessed on 20 April 2023). **Author Contributions:** [PERSON] defined the project pipeline and experiments, implemented the code for data processing and CNN training, made the figures, and helped with the manual segmentation protocol. [PERSON] was responsible for the data processing, data organization, and literature review. [PERSON] helped with the experimental planning, Python codes, and benchmark environment. [PERSON] helped with the data and evaluation metrics. [PERSON] performed all statistical analyses. [PERSON] performed manual data annotation and helped with the manual segmentation protocol. [PERSON] supervised the methods and data processing. [PERSON] supervised the overall development of the project. All authors have read and agreed to the published version of the manuscript. **Funding:** This project was partially supported by COOPERINT program, by the Sao Paulo Research Foundation (FAPESP-process 2013/07559-3), and by the National Council for Scientific and Technological Development (CNPq 313598/2020-7). [PERSON] was supported by CNPq (National Council for Scientific and Technological Development). [PERSON] appreciates grant #2019/21964-4, Sao Paulo Research Foundation (FAPESP). **Institutional Review Board Statement:** Ethical review and approval were waived for this study since it uses a public dataset. **Informed Consent Statement:** All subject recruitment procedures and informed consent forms, including consent to share de-identified data, were approved by the Washington University Institutional Review Board (IRB) [47]. Permission for using the Open Access data in the present study was obtained from the HCP.