Detection of masses and architectural distortions in digital breast tomosynthesis images using fuzzy and a contrario approaches

Palma, Giovanni; Bloch, Isabelle; Muller, Serge

doi:10.1016/j.patcog.2014.01.009

Cited by 35 publications

(36 citation statements)

References 31 publications

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“…e results of the DCNN model have shown the AUC of over 80% and the 80% Sen. Fotin et al [32] have developed a CAD framework of the DBT mass detection using a DCNN that is trained on the generated candidate region of interest (ROIs), which contains 1864 breast lesions in the mammography and 339 breast lesions from the DBT images data. It is reported that their model achieved an Acc of 86.40% and 89% Sen. e latent bilateral feature representations of masses in reconstructed DBT volumes o are classified with the DCNN model proposed by Kim et al [31], in which low-level features are [18], and Schie et al [17]. Chan et al [15] introduce three methods based on 2D and 3D, and the hybrid that combines 2D and 3D.…”

Section: Discussionmentioning

confidence: 99%

“…To optimize and make the technique suitable for DBT images, tomographic images were generated from reconstructed volume images for analysis. Palma et al [18] constructed a system of automatic detection of breast masses in DBT reconstruction images by using fuzzy theory and antagonistic reasoning method. Kim et al [19] studied the influence of the saliency of reconstructed slice images on the detection performance of breast masses in DBT reconstructed images and proposed an automatic detection method of breast masses based on the saliency of reconstructed slice images by DBT.…”

Section: Introductionmentioning

confidence: 99%

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DBT Masses Automatic Segmentation Using U-Net Neural Networks

Lai

Yang

2020

Computational and Mathematical Methods in Medicine

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To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DBT Masses Automatic Segmentation Using U-Net Neural Networks

Lai

Yang

2020

Computational and Mathematical Methods in Medicine

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“…(4) Subjective interpretation of cancer conspicuity consistently found that cancers were equally or more conspicuous in DBT compared with DM. Palma, et al [11] proposed a complete detection scheme for detecting masses and architectural distortions in DBT images. Two parts exist called channels, and each is dedicated to one type of lesion, which are then merged in a final decision step, thereby correctly handling the potential overlap between the two types of lesion.…”

Section: Dbt-related Workmentioning

confidence: 99%

Comparison Between Feature-Based and Convolutional Neural Network–Based Computer-Aided Diagnosis for Breast Cancer Classification in Digital Breast Tomosynthesis

Chan¹,

Yeh²

2019

JBR

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Digital breast tomosynthesis (DBT) is a promising new technique for breast cancer diagnosis. DBT has the potential to overcome the tissue superimposition problems that occur on traditional mammograms for tumor detection. However, DBT generates numerous images, thereby creating a heavy workload for radiologists. Therefore, constructing an automatic computer-aided diagnosis (CAD) system for DBT image analysis is necessary. This study compared feature-based CAD and convolutional neural network (CNN)-based CAD for breast cancer classification from DBT images. The research methods included image preprocessing, candidate tumor identification, three-dimensional feature generation, classification, image cropping, augmentation, CNN model design, and deep learning. The precision rates (standard deviation) of the LeNet-based CNN CAD and the feature-based CAD for breast cancer classification were 89.84 (0.013) and 84.46 (0.082), respectively. The T value was -4.091 and the P value was 0.00 < 0.05, which indicate that the LeNet-based CNN CAD significantly outperform the feature-based CAD. However, there is no significantly differences between the LeNet-based CNN CAD and the feature-based CAD on other criteria. The results can be applied to clinical medicine and assist radiologists in breast cancer identification.

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“…Since the introductory illustration of a-contrario methods on alignment detection [10], some recent works developed the underlying idea [18,7], but a large interest developed around using this fundamental pattern grounded in the Gestalt continuity principle in order to detect related elements such as segments [29,30,1], vanishing points [4,17] or scratches [19]. Concomitantly, acontrario methods have been developed for detecting more complex patterns, such as circles and ellipses [2,22] as well as coherent clusterings in a broader sense [28,8,25,26,21,32].…”

Section: Introductionmentioning

confidence: 99%

Efficient evaluation of the Number of False Alarm criterion

Hégarat‐Mascle

Aldea

Vandoni

2019

J Image Video Proc.

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This paper proposes a method for computing efficiently the significance of a parametric pattern inside a binary image. On the one hand, a-contrario strategies avoid the user involvement for tuning detection thresholds, and allow one to account fairly for different pattern sizes. On the other hand, a-contrario criteria become intractable when the pattern complexity in terms of parametrization increases. In this work, we introduce a strategy which relies on the use of a cumulative space of reduced dimensionality, derived from the coupling of a classic (Hough) cumulative space with an integral histogram trick. This space allows us to store partial computations which are required by the a-contrario criterion, and to evaluate the significance with a lower computational cost than by following a straightforward approach. The method is illustrated on synthetic examples on patterns with various parametrizations up to five dimensions. In order to demonstrate how to apply this generic concept in a real scenario, we consider a difficult crack detection task in still images, which has been addressed in the literature with various local and global detection strategies. We model cracks as bounded segments, detected by the proposed a-contrario criterion, which allow us to introduce additional spatial constraints based on their relative alignment. On this application, the proposed strategy yields state-of the-art results, and underlines its potential for handling complex pattern detection tasks.

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Detection of masses and architectural distortions in digital breast tomosynthesis images using fuzzy and a contrario approaches

Cited by 35 publications

References 31 publications

DBT Masses Automatic Segmentation Using U-Net Neural Networks

DBT Masses Automatic Segmentation Using U-Net Neural Networks

Comparison Between Feature-Based and Convolutional Neural Network–Based Computer-Aided Diagnosis for Breast Cancer Classification in Digital Breast Tomosynthesis

Efficient evaluation of the Number of False Alarm criterion

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