## 1 Introduction  
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. The corresponding investigations regarding neural circuitries of human brains also provide constructive consequences on research progress of connectome [14] and clinical diagnoses related to Alzheimer's and Parkinson's diseases [15]. Thus, among these applications, robust automatic methods of segmentation for large-scale medical imageries can efficiently save extensive and tedious manual interventions while dealing with humongous tissue labeling and clinical process setting. 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]. The automatic segmentation methods often can only have commonplace performance when there is a large ratio of the whole image size to the size of the desired tissue area. The tiny desired tissue area within an image and the poorly defined boundaries between the desired tissue area and its neighboring regions limit the feasibility of segmentation methods. For instance, since there is a large ratio of the whole brain tissue to the subthalamic nucleus, the image segmentation of the tiny subthalamic nucleus (approximately 6 mm \(\times\) 4 mm \(\times\) 5 mm) from their neighboring regions such as putamen, substantia nigra, zona incerta, and so forth [23], is always an open problem for the clinical investigations of deep brain stimulation. Besides, the contemporary MRI techniques for identification and localization of brain tissues, such as T2-weighted-imaging [24; 25], susceptibility weighted imaging [26; 27; 28], fluid-attenuated inversion recovery [29; 30; 31], and so on, often fail to distinguish desired minuscule tissues from their surrounding structures in clinical MRI processes. Furthermore, the anatomical information regarding the tiny tissues is only caught in few MR images during the acquirement processes. 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. Meanwhile, to reduce extensive and tedious manual interventions and for the convenience of visualization, the applied method should execute artificial intelligence approaches for automatically achieving the goals of image segmentation and pattern recognition. Considering the preprocessing of the medical imageries on data compression and denoising would be valuable for large-scale medical images to pursue further execution efficiency [11; 12; 32].