Title :
A system for the quantitative analysis of bone metastases by image segmentation
Author :
Erdi, Y.E. ; Humm, J.L. ; Imbriaco, M. ; Yeung, H. ; Larson, S.M.
Author_Institution :
Dept. of Med. Phys., Memorial Sloan-Kettering Cancer Center, New York, NY, USA
Abstract :
Preliminary evidence indicates that the fraction of bone containing metastatic lesions is a strong prognostic indicator of survival longevity for prostate and breast cancer. To quantify metastatic lesions, the most common method is to visually inspect the fraction of each bone involvement and determine the percent involvement by drawing region-of-interest. However, this approach is time-consuming, subjective and dependent upon individual interpretation. To overcome these problems, a semi-automated region-growing program was developed for the quantitation of metastases from planar bone scans. The program then computes the fraction of lesion involvement in each bone based on look-up-tables containing the relationship of bone weight with: race, sex, height, and age. The bone metastases analysis system has been used on 11 scans from 6 patients. The correlation was high (r=0.83) between conventional (manually drawn region-of-interest) and this analysis system. Bone metastases analysis results in consistently lower estimates of fractional involvement in bone compared to the conventional region-of-interest drawing or visual estimation method. This is due to the apparent broadening of objects at and below the limits of resolution of the gamma camera. Bone metastases (BMets) analysis system reduces the delineation and quantitation time of lesions by at least 2 compared to manual region-of-interest drawing. The objectivity of this technique allows the detection of small variations in follow-up patient scans for which manual region-of-interest method may fail, due to performance variability of the user. This method preserves the diagnostic skills of the nuclear medicine physician to select which bony structures contain lesions, yet combines it with an objective delineation of the lesion.
Keywords :
bone; image segmentation; medical image processing; radioisotope imaging; bone metastases quantitative analysis; bone weight; bony structures; diagnostic skills; lesion involvement fraction; lesions; look-up-tables; manually drawn region-of-interest; medical diagnostic imaging; nuclear medicine image segmentation; objective delineation; planar bone scans; semi-automated region-growing program; Biomedical imaging; Bone diseases; Cancer; Image analysis; Image segmentation; Imaging phantoms; Lesions; Metastasis; Neoplasms; Physics;
Conference_Titel :
Nuclear Science Symposium, 1996. Conference Record., 1996 IEEE
Conference_Location :
Anaheim, CA, USA
Print_ISBN :
0-7803-3534-1
DOI :
10.1109/NSSMIC.1996.587985