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Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

J Lee

J Lee1*, R Nishikawa1 , I Reiser2 , J Boone3 , (1) University of Pittsburgh, Pittsburgh, PA, (2) The University of Chicago, Chicago, IL, (3) UC Davis Medical Center, Sacramento, CA


WE-G-207-5 (Wednesday, July 15, 2015) 4:30 PM - 6:00 PM Room: 207

Purpose:Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis.

Methods:A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared.

Results:Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance.

Conclusion:There are certain images that yield better segmentation or classification performance. The best segmentation result does not necessarily lead to the best classification result.

Funding Support, Disclosures, and Conflict of Interest: This work has been supported in part by grants from the NIH R21-EB015053. R Nishikawa is receives royalties form Hologic, Inc.

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