Accurate segmentation of the thalamus and its nuclei is a prerequisite for studying anatomical connectivity and its correlation to neurological diseases. The probabilistic tractography pipeline in FSL is commonly used for thalamus connectivity-based parcellation. However, dMRI data analysis and tractography are done in a mix of standard and subject spaces which can bias anatomical connectivity findings. Here, we presented a framework that improves thalamus parcellation by performing DW data processing and probabilistic tractography in the subject’s native space, as well by generating population- connectivity priors. Higher segmentation accuracy was achieved with it when compared to FSL’s available pipeline.