Automatic localization of the subthalamic nucleus on patient‐specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation

Abstract

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson’s disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

Deep-learning based fully automatic segmentation of the globus pallidus interna and externa using ultra-high 7 Tesla MRI

“…subthalamic nucleus (STN) or GPi) is directly correlated with the success of the DBS procedure and reduction of adverse effects (Ellis et al, 2008;Marks et al, 2009;Paek et al, 2013;Patel et al, 2015;Richardson et al, 2009;Rolston et al, 2016;Welter et al, 2014). Correct anatomical target identification is characterized not only by the target's center of mass, but also by its boundaries (Kim et al, 2019). Thus, precise identification of both GPe and GPi and their lamina boundary is of great importance.…”

“…Although atlases, which are typically defined in a normalized space, have shown great importance in retrospective population studies Horn, Neumann, et al, 2017;Horn, Reich, et al, 2017;Kim et al, 2019), several recent studies have shown that variablity exists of the size and shape of deep brain structures between different subjects (Abosch et al, 2010;Duchin et al, 2018;Lenglet et al, 2012;Patriat et al, 2018). This inherent inter-patient variability is not fully captured by atlas-based segmentations, as they provide a single, averaged shape of brain structures, be it deterministic or probabilistic.…”

Medical Image Segmentation with Adjustable Computational Complexity Using Data Density Functionals

“…Automatic identification and segmentation of medical imageries benefit the planning and guidance of modern surgery [1][2][3][4][5], clinical investigations [6][7][8][9][10], rehabilitation [11][12][13], and so forth. High-quality reconstructive anatomical morphology provides convenience on surgery planning and the understanding between organ functionalities and pathological diagnosis.…”

“…Among those developed techniques, state-of-the-art methodologies on the field of magnetic resonance image (MRI) processing successfully combined several merits from interdisciplinary methods [16][17][18] and exhibit an opportunity to track brain regions related to relevant diseases [19,20]. For instance, precise identification and segmentation of subthalamic nucleus from three-dimensional medical imageries facilitate the automatic planning of deep brain stimulation, and the clinical result has also shown clinical potential for relieving the motor symptoms of advanced Parkinson's disease [8,21,22].…”

“…For physically localizing the subthalamic nucleus in the procedure of deep brain stimulation, a compensation method is to utilize the technique of intraoperative microelectrode recording to extract electric signal differences between the subthalamic nucleus and its neighboring regions. However, this method requires significant neurosurgical expertise and lengthens the surgery duration [8,23]. Therefore, to solve these essential problems in medical applications, the adopted methods should focus on the identification and the localization of the tiny desired anatomical tissues and their surrounding regions by only utilizing the information acquired from medical images.…”