OBJECTIVE: To develop a completely automated method, based on image processing techniques and artificial neural networks, for the interpretation of bone scans regarding the presence or absence of metastases. METHODS: A total of 200 patients, all of whom had the diagnosis of breast or prostate cancer and had undergone bone scintigraphy, were studied retrospectively. Whole-body images, anterior and posterior, were obtained after injection of 99mTc-methylene diphosphonate. The study material was randomly divided into a training group and a test group, with 100 patients in each group. The training group was used in the process of developing the image analysis techniques and to train the artificial neural networks. The test group was used to evaluate the automated method. The image processing techniques included algorithms for segmentation of the head, chest, spine, pelvis and bladder, automatic thresholding and detection of hot spots. Fourteen features from each examination were used as input to artificial neural networks trained to classify the images. The interpretations by an experienced physician were used as the 'gold standard'. RESULTS: The automated method correctly identified 28 of the 31 patients with metastases in the test group, i.e., a sensitivity of 90%. A false positive classification of metastases was made in 18 of the 69 patients not classified as having metastases by the experienced physician, resulting in a specificity of 74%. CONCLUSION: A completely automated method can be used to detect metastases in bone scans. Future developments in this field may lead to clinically valuable decision-support tools.
OBJECTIVE: To develop a completely automated method, based on image processing techniques and artificial neural networks, for the interpretation of bone scans regarding the presence or absence of metastases. METHODS: A total of 200 patients, all of whom had the diagnosis of breast or prostate cancer and had undergone bone scintigraphy, were studied retrospectively. Whole-body images, anterior and posterior, were obtained after injection of 99mTc-methylene diphosphonate. The study material was randomly divided into a training group and a test group, with 100 patients in each group. The training group was used in the process of developing the image analysis techniques and to train the artificial neural networks. The test group was used to evaluate the automated method. The image processing techniques included algorithms for segmentation of the head, chest, spine, pelvis and bladder, automatic thresholding and detection of hot spots. Fourteen features from each examination were used as input to artificial neural networks trained to classify the images. The interpretations by an experienced physician were used as the 'gold standard'. RESULTS: The automated method correctly identified 28 of the 31 patients with metastases in the test group, i.e., a sensitivity of 90%. A false positive classification of metastases was made in 18 of the 69 patients not classified as having metastases by the experienced physician, resulting in a specificity of 74%. CONCLUSION: A completely automated method can be used to detect metastases in bone scans. Future developments in this field may lead to clinically valuable decision-support tools.
Authors: Matthew S Brown; Gregory H Chu; Hyun J Kim; Martin Allen-Auerbach; Cheryce Poon; Juliette Bridges; Adria Vidovic; Bharath Ramakrishna; Judy Ho; Michael J Morris; Steven M Larson; Howard I Scher; Jonathan G Goldin Journal: Nucl Med Commun Date: 2012-04 Impact factor: 1.690
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