## 1 Introduction  
The thalamus (Figure 1) is a bilateral subcortical brain structure. Each of its two parts measure on average 20 mm in the left-right direction (radiological convention) and 30 mm in the anterior-posterior direction [1]. It is located in the central region of the brain, below the ventricles and fornix, and above the hypothalamus (Figures 1 and 2). Despite its relatively small volume, the thalamus can be further divided into nuclei depending on the cytological differences existing across different regions of the same structure [1]. The thalamus also has a central physiological role in the nervous system, functioning as a signal transponder owing to the white matter connecting it to a wide area of the cortex. Furthermore, it is involved in other functions, including the regulation of sleep, alertness, motor functions, and spoken language [1, 2]. Many neurological disorders are associated with thalamic changes. This region of the brain includes targets for deep-brain stimulation and stereotactic ablation for the treatment of symptoms of conditions such as Parkinson's disease, essential tremors, epilepsy, chronic pain syndrome, and multiple sclerosis [3, 4]. In the context of surgical planning, accurate segmentation is essential for the success of therapy. Relevant surgical approaches include focused ultrasound thalamotomy, where the segmentation requirement is the rapidity of its estimation for improving operation efficacy [5]. Accurate segmentation is also highly  
Magnetic resonance imaging (MRI) is frequently employed to visualize the thalamus, as it enables the non-invasive observation of in vivo deep brain structures under safe conditions for the patient. A wide variety of automated tools are available for MRI-based thalamus segmentation. Nonetheless, manual segmentation is still considered the \"gold standard\", as it overcomes the low contrast and resolution achievable in standard 1.5 and 3T T1-weighted acquisitions [7]. In fact, these limitations in contrast and resolution may constitute a bias, thereby yielding suboptimal segmentation results. Unfortunately, manual segmentation has its own limitations: discrepancies owing to the scarcity of protocols [8; 9; 10]; the need for trained raters; and an excessively large execution time [7], especially for larger datasets. Consequently, the development of faster and more robust automatic thalamus-segmentation methods is crucial. The T1-weighted scan is one of the most widespread MRI sequences owing to the rapidity and relative simplicity of its acquisition, visual similarity with anatomical slices, and good spatial resolution [11; 12]. For these reasons, it is also one of the most used MRI types with both manual and automated segmentation methods. In the field of neurological structures, existing automated segmentation methods can be primarily categorized as atlas-based or deep-learning-based. As atlas-based methods, the fsl_anat [13] tool of the FSL software and FreeSurfer [14] represent atlas-guided thalamus segmentation approaches, serving as the most popular automated tools, despite their time-intensive underlying algorithms [15] and tendency to overestimate the thalamic shape compared to manual segmentation [7]. Recently, a less common structural acquisition of white-matter-nulled MP-RAGE images has been employed to collect data using a 7T MRI scanner, leading to the development of Thalamus-Optimized Multi-Atlas Segmentation (THOMAS) [1]. Theoretically, such a sequence would enhance the thalamic contrast on a 7T MRI scanner. THOMAS has been successfully used to segment 12 thalamic nuclei with Dice coefficients of 0.85 and 0.7 for large and small nuclei, respectively. Unfortunately, only qualitative results were produced for its feasibility on 3T MRI, whereas quantitative validation is necessary given the known complexity of the task [4; 16]. Methods built upon deep learning strategies allow for fast thalamic segmentation, at the cost of requiring a large quantity of data for training in order to avoid overfitting [17; 18].