| Parkinson’s disease (PD) is a common neurodegenerative disease in the centralnervous system which is characterized by static tremor, muscle stiffness, motionstarting difficulty, and postural reflex impairment. Because of its high morbidity anddisability rate, it has become one of the major diseases threatening the health of elderpopulation. In recent years, with the development and combined application ofnervous stereotactic surgery (STS), imaging technology and computer technology, thebrain STS becomes one of the most effective methods for the treatment of PD, withincreasing popularity. The advantages of STS include little trauma, high safety, andrapid recovery, etc., with no need for craniotomy or general anesthesia. The targetnuclei (TN) must be localized at first in the implementation of brain STS to identifythe coordinate of the target in the stereotactic framework, and then drilling isconducted for irritation or derogation. Therefore, the exact location of TN is a keystep in the surgical treatment of PD, which directly determines the efficacy of surgery.In this paper, the automatic identification technique of PD TN was studied. Adependency tree model was established for identifying PD TN according to thecorrelative information of deep brain structures. This model contained five keysub-structures and three PD TN. Each structure was identified one by one. Moreover,a software system for identification of PD TN was designed. The main contentsinclude the following three parts.(1) Segmentation and3D reconstruction of PD TN using AmiraThe segmentation and3D reconstruction of key sub-structures and TN in PDwere performed with manual segmentation, using Amira3.1,3D visualization softwaredeveloped by French TGS Company. Segmentation and visualization processes wereexplored preliminarily.(2) Study on automatic identification algorithm research of PD TN based on fuzzyconnectedness (FC)The key sub-structures and TN in PD are small, obscure, and difficult toaccurately identify in MR images, seriously affecting the precision of segmentation.Hence, the manual method is usually used to identify the structures clinically, but it istime-consuming, and with a certain degree of subjectivity which makes segmentationresults varying from person to person. This paper explored a new method forautomatically identifying the key sub-structures and TN in PD. An improved FCsegmentation method was used to segment the key sub-structures and TN. Comparedwith the traditional FC method, the new method improved the segmentation accuracy.Compared with the gold standard marked by clinicians, the similarity rate ofsegmentation results was larger than80%, which may have a potential for clinical application.(3) PD TN identification system designA PD TN identification system was designed using the Visual C++6.0, theMedical Imaging ToolKit (MITK), and the Windows XP operating system. Thissystem integrates reading and viewing, segmenting and3D visualizing capabilities fora variety of medical image formats, with the features of easy operation and highinteraction. Experimental results indicate that the system is effective to realize thesegmentation and3D reconstruction of the key sub-structures and TN in PD.Reconstruction results are promising, and the coordinates of the TN center can becalculated in the system, which can assist clinicians in locating PD TN beforeimplementing the surgery.This paper realized segmentation and3D visualization of TN in surgicaltreatment of PD, which will provide more scientific and intuitive imaging localizationreference for clinicians, with much clinical significance in the aspects of improvingthe curative efficacy and reducing the risk of surgery. |
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