Boundary detection of retinoblastoma tumors with neural networks.
Consistent and accurate measurement of retinoblastoma tumors is of important clinical value for treatment management. This paper presents an algorithm for the determination of retinoblastoma (RB) tumor to assist in the determination of tumor volume changes throughout treatment periods. The result of the development of a neural network approach for the analysis of three-dimensional ultrasound images shows that it is possible to identify retinoblastoma tumors and accurately determine the front and back boundary of the tumor. The algorithm used was a soft competitive learning network with two inputs. The outputs of the network identify the eye, the tumor, and the back of the eye.
Chai
M I
MI
Department of Mechanical and Industrial Engineering, University of Toronto, M5S 3G8, Toronto, ON.
Chai
A
A
Sullivan
P
P
D000465:Algorithms; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D016571:Neural Networks, Computer; D012175:Retinoblastoma / Q000000981:diagnostic imaging* / Q000235:genetics / Q000473:pathology; D014463:Ultrasonography