| Summary: | 博士 === 義守大學 === 資訊工程學系 === 104 === In recent years, whole body bone scan imaging (WBBS) has become an important and widespread diagnostic tool in nuclear medicine due to its high sensitivity and relatively low cost. WBBS is particularly important because it can identify bony metastasis; however, it is limited in some cases; for example, osteolytic bony lesions. Additional factors, such as patients’ individual differences, poor image quality, and doctor experiences, can bias the interpretation of WBBS and affect the accuracy of diagnosis and treatment.
Therefore, the development of a computer-aided diagnosis (CAD) system to provide objective and quantitative analysis for WBBS is an important clinical research issue. In our study, we developed an automated detection system – the abnormal flow browser irregular flux viewer (IFV), with the ability to automatically locate abnormal flow in bony lesions; this tool could provide assistance to physician diagnosis and give a prediction value in bone metastasis. The system was developed in two stages. In the first stage, we tried to perform “non-supervision type of neural network training” to find the gradient and kinetic energy of the index value. Bone scan images of three types of cancer patients (prostate, lung, and breast cancers) were collected. The bone scan results were categorized into four groups (No Metastasis, degenerative arthritis, slight bony metastasis, or serious bony metastasis). Using Gradient Vector Flow, we assessed different areas of bone image pixels to calculate the values of gradient and momentum for adaptive threshold. In the second stage, we used View-Tool (an abnormal flow browser for assessing the abnormal flow point of the clustering analysis) to correct the image histogram in order to obtain “self-cluster” and “union-cluster” indexes, according to the correlated distance from the centroid to distinguish abnormal flow accumulated points (hot spots). Then, the hot spots of the pixels were labeled as the suspected lesions. We tried to compare the clinical diagnostic reports of CT, MRI, SPECT/CT, and PET/CT with IFV-BS reports. Our proposed approach had a higher sensitivity to improve the inherent limits of osteolytic lesion in planar bone scintigraphy. The corresponding results show sensitivity to predict skeletal metastasis in prostate cancer (93 %) [95 % confidence interval (CI) 0.91~0.93], breast cancer (91 %) [95 % CI 0.90~0.92], and lung cancer (83 %) [95 % CI 0.82~0.84]. The results of our study showed that our abnormal flow browser is reliable and may provide assistance for image interpretation and generate prediction values in WBBS.
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