Automated localization of breast cancer in DCE-MRI

Gubern-Mérida, Albert; Martí, Robert; Melendez, Jaime; Hauth, Jakob L.; Mann, Ritse M.; Karssemeijer, Nico; Platel, Bram

doi:10.1016/j.media.2014.12.001

Cited by 114 publications

(90 citation statements)

References 41 publications

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“…To characterize the kinetic features, maximum enhancement (ME), time to peak (TP), uptake rate (UR), washout rate (WR), and area under the curve are extracted from the time-intensity curve of each voxel. These kinetic features are computed according to the relative signal enhancement [14]:…”

Section: False Positive Reductionmentioning

confidence: 99%

“…For each voxel in the potential lesion region, ME, TP, UR, and WR are defined here [14,21] as follows:…”

Section: False Positive Reductionmentioning

confidence: 99%

“…However, their approach is validated on a smaller number of cases compared to other studies. Gubern-Mérida et al [14] validate their method on 209 MRI cases, which is larger than the other datasets. However, the mentioned dataset does not include benign lesions while the discrimination of benign lesions from the normal breast tissues is one of the challenging issues in a CAD system.…”

Section: False Positive Reductionmentioning

confidence: 99%

“…It is shown that 20-30% of all invasive lesions and majority of the DCIS lesions are non-mass-like enhancement [13]. To date, only GubernMérida et al [14] offer an automated approach for localizing both mass and non-mass-like enhancing lesions in breast DCE-MRI. In their method, lesion candidates are located based on the blob and relative enhancement voxel features.…”

Section: Introductionmentioning

confidence: 99%

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Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter

Shokouhi

Fooladivanda

Ahmadinejad

2017

EURASIP J. Adv. Signal Process.

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A computer-aided detection (CAD) system is introduced in this paper for detection of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed CAD system firstly compensates motion artifacts and segments the breast region. Then, the potential lesion voxels are detected and used as the initial seed points for the seeded region-growing algorithm. A new and robust region-growing algorithm incorporating with Fuzzy C-means (FCM) clustering and vesselness filter is proposed to segment any potential lesion regions. Subsequently, the false positive detections are reduced by applying a discrimination step. This is based on 3D morphological characteristics of the potential lesion regions and kinetic features which are fed to the support vector machine (SVM) classifier. The performance of the proposed CAD system is evaluated using the free-response operating characteristic (FROC) curve. We introduce our collected dataset that includes 76 DCE-MRI studies, 63 malignant and 107 benign lesions. The prepared dataset has been used to verify the accuracy of the proposed CAD system. At 5.29 false positives per case, the CAD system accurately detects 94% of the breast lesions.

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Section: False Positive Reductionmentioning

confidence: 99%

“…For each voxel in the potential lesion region, ME, TP, UR, and WR are defined here [14,21] as follows:…”

Section: False Positive Reductionmentioning

confidence: 99%

Section: False Positive Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter

Shokouhi

Fooladivanda

Ahmadinejad

2017

EURASIP J. Adv. Signal Process.

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“…However, the structural complexity of tissues and organs, or the tremendous numbers of images makes the manual delineation wearisome or even impossible (Valverde et al, 2017;Lladó et al, 2012). As a practicable solution, automated analysis is nowadays generally accepted by medical community and has been widely used for clinical diagnoses (J.-H. Lee, Marzelli, Jolesz, & Yoo, 2009;Gubern-Mérida et al, 2015;Weese & Lorenz, 2016).…”

Section: Introductionmentioning

confidence: 99%

A review of Methods for Bias Correction in Medical Images

Song

Zheng

2017

BMER

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Bias field in medical images is an undesirable artifact primarily arises from the improper image acquisition process or the specific properties of the imaged object. This artifact can be characterized by a smooth variation of intensities across the image and significantly degrade many medical image analysis techniques. Studies on bias correction have been investigated extensively over these years. In this paper, we proposed to category and analysis existing bias correction methods, provide a complete review article that enables comparative studies on bias correction in medical images.

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Assessing the performance of benign and malignant breast lesion classification with bilateral TIC differentiation and other effective features in DCE‐MRI

Sun

Liu

et al. 2019

Magnetic Resonance Imaging

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Background Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is increasingly being used to detect and diagnose breast cancer. However, the effective methods based on quantitative feature analysis need to be explored. Purpose To investigate a new approach for improving the performance of benign and malignant breast lesion classification by combining bilateral time–intensity curve (TIC) differentiation features with other effective features. Study Type Retrospective. Population In all, 112 DCE‐MRI biopsy‐proven breast examinations (45 malignant tumors and 67 benign tumors). Field Strength/Sequence 3 T MR with a breast coil. Precontrast images were acquired before contrast administration. Subsequently, postcontrast images were acquired approximately every minute directly following administration of the contrast agent (gadopentetate dimeglumine). The postcontrast images contain eight sequences in total. Each sequence contains 78 slices. Assessment: Accuracy, sensitivity, specificity, and the area under the curve (AUC) of the classification was calculated and compared with the published result. Statistical Tests Five‐fold crossvalidation was used. Results Areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.8461, 0.7914, 0.8514, and 0.9058 were achieved using conventional features, unilateral TIC features, bilateral TIC differentiation features, and all the selected features, respectively. In terms of accuracy, the use of only unilateral TIC features or conventional features achieved an accuracy of 0.7321 or 0.8482 (sensitivity of 0.7556 or 0.7111 and specificity of 0.7164 or 0.9403). However, the accuracy increased to 0.9196 (sensitivity of 0.8889 and specificity of 0.9403) when bilateral TIC differentiation features were included. Data Conclusion Bilateral differentiation TIC features can serve as a stronger indicator in differentiating benign and malignant breast lesions than the unilateral TIC features computed from one side of the breast only and some conventional features that are commonly used in MR image analysis. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:465–473.

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Automated localization of breast cancer in DCE-MRI

Cited by 114 publications

References 41 publications

Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter

Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter

A review of Methods for Bias Correction in Medical Images

Assessing the performance of benign and malignant breast lesion classification with bilateral TIC differentiation and other effective features in DCE‐MRI

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