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

Shokouhi, Shahriar B.; Fooladivanda, Aida; Ahmadinejad, Nasrin

doi:10.1186/s13634-017-0476-x

Cited by 15 publications

(9 citation statements)

References 29 publications

(42 reference statements)

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“…Despite its high sensitivity, breast DCE-MRI requires standardized acquisition protocols and longer time for image processing and interpretation and has variable specificity. Indeed, the evaluation of a large quantity of 4D-DCE images for each patient is a time-consuming process, and their interpretation requires experienced radiologists [ 14 ]. In literature, textural analysis techniques have been applied to dynamic breast MRI to quantify BPE [ 15 ], discriminate malignant from benign tissue [ 16 – 18 ], or identify particular topology of lesions [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Gradient-Based Approach for Breast DCE-MRI Analysis

Losurdo

Basile

Fanizzi

et al. 2018

BioMed Research International

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Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.

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Section: Introductionmentioning

confidence: 99%

A Gradient-Based Approach for Breast DCE-MRI Analysis

Losurdo

Basile

Fanizzi

et al. 2018

BioMed Research International

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“…This boundary is amended using a muscle-slab model that is a curved slab with different thicknesses at distinctive locations. Some studies also used the thresholding and morphological operations for breast isolation [11][12][13]. Giannini et al [14] segmented the breast region from the MRI images by sign of gradients.…”

Section: Introductionmentioning

confidence: 99%

Breast-region segmentation in MRI using chest region atlas and SVM

Fooladivanda¹,

Shokouhi²,

Ahmadinejad³

2017

Turk J Elec Eng & Comp Sci

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An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation of the breast region. Due to the similar signal intensity of fibroglandular tissue and the chest wall, the segmentation process is difficult for breasts with fibroglandular tissue connected to the chest wall. In order to overcome this challenge, a new framework is presented that relies on a chest region atlas. The proposed method first detects the approximated breast-chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the connectivity of fibroglandular tissue to the chest wall by the extracted features from the obtained breast-chest wall boundary. Finally, the obtained breast-chest wall boundary is accurately refined using the geometric shape of the chest region, which is obtained by an atlas-based segmentation method. The proposed method is validated using a dataset of 5964 breast MRI images from 126 women. The Dice similarity coefficient (DSC), total overlap (TO), false negative (FN), and false positive (FP) values are calculated to measure the similarity between automatic and manual segmentation results. Our method achieves DSC, TO, FN, and FP values of 96.46%, 96.41%, 3.59%, and 3.51%, respectively. The results prove the effectiveness of the presented algorithm for breasts with different sizes, shapes, and density patterns.

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“…Renz et al [26] exploited adaptive thresholding to obtain high sensitivity; however, ground-truth annotations were not provided in their method, and the evaluation was performed by visual inspection. Shokouhi et al [19] applied fuzzy c-means clustering. They reported a slightly higher detection rate than ours, at a different false-positive rate, and Vignati et al [10] utilized a normalization-based method that achieved similar performance as the proposed method; both used a smaller dataset than ours.…”

Section: Discussionmentioning

confidence: 99%

Automatic breast lesion detection in ultrafast DCE‐MRI using deep learning

et al. 2021

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We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the 3D spatial information and temporal information obtained from the early-phase of the dynamic acquisition. Methods: The proposed CADe method, based on a modified 3D RetinaNet model, operates on ultrafast T1 weighted sequences, which are preprocessed for motion compensation, temporal normalization, and are cropped before passing into the model. The model is optimized to enable the detection of relatively small breast lesions in a screening setting, focusing on detection of lesions that are harder to differentiate from confounding structures inside the breast. Results: The method was developed based on a dataset consisting of 489 ultrafast MRI studies obtained from 462 patients containing a total of 572 lesions (365 malignant,207 benign) and achieved a detection rate,sensitivity,and detection rate of benign lesions of 0.90 (0.876-0.934), 0.95 (0.934-0.980), and 0.81 (0.751-0.871) at four false positives per normal breast with 10-fold cross-testing, respectively. Conclusions:The deep learning architecture used for the proposed CADe application can efficiently detect benign and malignant lesions on ultrafast DCE-MRI. Furthermore, utilizing the less visible hard-to-detect lesions in training improves the learning process and, subsequently, detection of malignant breast lesions.

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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

Cited by 15 publications

References 29 publications

A Gradient-Based Approach for Breast DCE-MRI Analysis

A Gradient-Based Approach for Breast DCE-MRI Analysis

Breast-region segmentation in MRI using chest region atlas and SVM

Automatic breast lesion detection in ultrafast DCE‐MRI using deep learning

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