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Proceedings Paper

Application of artificial neural network in simulating subjective evaluation of tumor segmentation
Author(s): Dongjiao Lv; Xiang Deng
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Paper Abstract

Systematic validation of tumor segmentation technique is very important in ensuring the accuracy and reproducibility of tumor segmentation algorithm in clinical applications. In this paper, we present a new method for evaluating 3D tumor segmentation using Artificial Neural Network (ANN) and combined objective metrics. In our evaluation method, a three-layer feed-forwarding backpropagation ANN is first trained to simulate radiologist's subjective rating using a set of objective metrics. The trained neural network is then used to evaluate the tumor segmentation on a five-point scale in a way similar to expert's evaluation. The accuracy of segmentation evaluation is quantified using average correct rank and frequency of the reference rating in the top ranks of simulated score list. Experimental results from 93 lesions showed that our evaluation method performs better than individual metrics. The optimal combination of metrics from normalized volume difference, volume overlap, Root Mean Square symmetric surface distance and maximum symmetric surface distance showed the smallest average correct rank (1.43) and highest frequency of the reference rating in the top two places of simulated rating list (93.55%). Our results also demonstrate that the ANN based non-linear combination method showed better evaluation accuracy than linear combination method in all performance measures. Our evaluation technique has the potential to facilitate large scale segmentation validation study by predicting radiologists rating, and to assist development of new tumor segmentation algorithms. It can also be extended to validation of segmentation algorithms for other applications.

Paper Details

Date Published: 3 March 2011
PDF: 7 pages
Proc. SPIE 7966, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment, 79661H (3 March 2011); doi: 10.1117/12.877892
Show Author Affiliations
Dongjiao Lv, Siemens Ltd. China (China)
Xiang Deng, Siemens Ltd. China (China)

Published in SPIE Proceedings Vol. 7966:
Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment
David J. Manning; Craig K. Abbey, Editor(s)

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