Evaluation of a 3D lesion segmentation algorithm on DBT and breast CT images

Reiser, Ingrid; Joseph, Seena; Nishikawa, Robert M.; Giger, Maryellen L.; Boone, John M.; Lindfors, Karen K.; Edwards, Alexandra; Packard, N.; Moore, Richard H.; Kopans, Daniel B.

doi:10.1117/12.844484

Cited by 12 publications

(25 citation statements)

References 7 publications

(5 reference statements)

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“…Reiser et al extended it into 3D and showed that it can be applied on dedicated breast CT images [8]. For a given lesion contour dΩ, the 3D RGI is given by:…”

Section: Contour Initializationmentioning

confidence: 99%

“…In this algorithm, the standard deviation of the Gaussian constraint function was 10 mm, based on Reiser et al's study [8]. Further, to ensure that the initial contour is completely contained within the lesion before the second segmentation stage, morphological erosion is applied to shrink the RGI segmented lesion contour by using the MATLAB function "imerode" with a cubic structuring element.…”

Section: Contour Initializationmentioning

confidence: 99%

“…Based on our previous study [8], RGI can produce an approximate contour in a very short amount of time, with the limitation of the contours being too spherical and sometimes undergrown. While these limitations can produce unsatisfactory lesion outlines, they make the RGI algorithm well-suited for contour initialization, which requires the contour to be entirely contained within the lesion.…”

Section: Image Datasetmentioning

confidence: 99%

“…The algorithm presented here requires only the lesion center as input, which is provided by a radiologist but could alternatively result from the output of a lesion detection (CADe) algorithm. Other than our initial study [8], to the best of our knowledge, this is the first algorithm for automated seeded lesion segmentation on bCT.…”

Section: Introductionmentioning

confidence: 99%

“…In an initial study, masses in bCT images were segmented using the radial gradient index (RGI) algorithm [8,9]. For 93 % of the masses, the automated segmentation yielded an overlap ratio of 0.4 or greater.…”

Section: Introductionmentioning

confidence: 99%

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Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria

et al. 2013

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Dedicated breast CT (bCT) produces highresolution 3D tomographic images of the breast, fully resolving fibroglandular tissue structures within the breast and allowing for breast lesion detection and assessment in 3D. In order to enable quantitative analysis, such as volumetrics, automated lesion segmentation on bCT is highly desirable. In addition, accurate output from CAD (computeraided detection/diagnosis) methods depends on sufficient segmentation of lesions. Thus, in this study, we present a 3D lesion segmentation method for breast masses in contrastenhanced bCT images. The segmentation algorithm follows a two-step approach. First, 3D radial-gradient index segmentation is used to obtain a crude initial contour, which is then refined by a 3D level set-based active contour algorithm. The data set included contrast-enhanced bCT images from 33 patients containing 38 masses (25 malignant, 13 benign). The mass centers served as input to the algorithm. In this study, three criteria for stopping the contour evolution were compared, based on (1) the change of region volume, (2) the average intensity in the segmented region increase at each iteration, and (3) the rate of change of the average intensity inside and outside the segmented region. Lesion segmentation was evaluated by computing the overlap ratio between computer segmentations and manually drawn lesion outlines.For each lesion, the overlap ratio was averaged across coronal, sagittal, and axial planes. The average overlap ratios for the three stopping criteria ranged from 0.66 to 0.68 (dice coefficient of 0.80 to 0.81), indicating that the proposed segmentation procedure is promising for use in quantitative dedicated bCT analyses.

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“…Reiser et al extended it into 3D and showed that it can be applied on dedicated breast CT images [8]. For a given lesion contour dΩ, the 3D RGI is given by:…”

Section: Contour Initializationmentioning

confidence: 99%

Section: Contour Initializationmentioning

confidence: 99%

Section: Image Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria

et al. 2013

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Optimal reconstruction and quantitative image features for computer‐aided diagnosis tools for breast CT

et al. 2017

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Purpose The purpose of this study is to determine the optimal representative reconstruction and quantitative image feature set for a computer-aided diagnosis (CADx) scheme for dedicated breast computer tomography (bCT). Method We used 93 bCT scans that contain 102 breast lesions (62 malignant, 40 benign). Using an iterative image reconstruction (IIR) algorithm, we created 37 reconstructions with different image appearances for each case. In addition, we added a clinical reconstruction for comparison purposes. We used image sharpness, determined by the gradient of gray value in a parenchymal portion of the reconstructed breast, as a surrogate measure of the image qualities/appearances for the 38 reconstructions. After segmentation of the breast lesion, we extracted 23 quantitative image features. Using leave-one-out cross-validation (LOOCV), we conducted the feature selection, classifier training, and testing. For this study, we used the linear discriminant analysis classifier. Then, we selected the representative reconstruction and feature set for the classifier with the best diagnostic performance among all reconstructions and feature sets. Then, we conducted an observer study with six radiologists using a subset of breast lesions (N = 50). Using 1000 bootstrap samples, we compared the diagnostic performance of the trained classifier to those of the radiologists. Result The diagnostic performance of the trained classifier increased as the image sharpness of a given reconstruction increased. Among combinations of reconstructions and quantitative image feature sets, we selected one of the sharp reconstructions and three quantitative image feature sets with the first three highest diagnostic performances under LOOCV as the representative reconstruction and feature set for the classifier. The classifier on the representative reconstruction and feature set achieved better diagnostic performance with an area under the ROC curve (AUC) of 0.94 (95% CI = [0.81, 0.98]) than those of the radiologists, where their maximum AUC was 0.78 (95% CI = [0.63, 0.90]). Moreover, the partial AUC, at 90% sensitivity or higher, of the classifier (pAUC = 0.085 with 95% CI = [0.063, 0.094]) was statistically better (p-value < 0.0001) than those of the radiologists (maximum pAUC = 0.009 with 95% CI = [0.003, 0.024]). Conclusion We found that image sharpness measure can be a good candidate to estimate the diagnostic performance of a given CADx algorithm. In addition, we found that there exists a reconstruction (i.e., sharp reconstruction) and a feature set that maximizes the diagnostic performance of a CADx algorithm. On this optimal representative reconstruction and feature set, the CADx algorithm outperformed radiologists.

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Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification

et al. 2018

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Evaluation of a 3D lesion segmentation algorithm on DBT and breast CT images

Cited by 12 publications

References 7 publications

Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria

Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria

Optimal reconstruction and quantitative image features for computer‐aided diagnosis tools for breast CT

Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification

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