Classification of breast lesions using segmented quantitative ultrasound maps of homodyned <i>K</i> distribution parameters

Byra, Michał; Nowicki, Andrzej; Wróblewska-Piotrzkowska, Hanna; Dobruch‐Sobczak, Katarzyna

doi:10.1118/1.4962928

Cited by 53 publications

(38 citation statements)

References 57 publications

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“…For each mass, two perpendicular scans were recorded. The OASBUD was originally used to assess the statistical properties of backscattered ultrasound echoes in breast tissue and to differentiate breast masses using transfer learning with CNNs . Detailed descriptions of both datasets can be found in the original papers …”

Section: Methodsmentioning

confidence: 99%

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

et al. 2019

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Purpose We propose a deep learning‐based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment protocol. Methods Several transfer learning techniques are employed to develop classifiers based on a set of 882 ultrasound images of breast masses. Additionally, we introduce the concept of a matching layer. The aim of this layer is to rescale pixel intensities of the grayscale ultrasound images and convert those images to red, green, blue (RGB) to more efficiently utilize the discriminative power of the convolutional neural network pretrained on the ImageNet dataset. We present how this conversion can be determined during fine‐tuning using back‐propagation. Next, we compare the performance of the transfer learning techniques with and without the color conversion. To show the usefulness of our approach, we additionally evaluate it using two publicly available datasets. Results Color conversion increased the areas under the receiver operating curve for each transfer learning method. For the better‐performing approach utilizing the fine‐tuning and the matching layer, the area under the curve was equal to 0.936 on a test set of 150 cases. The areas under the curves for the radiologists reading the same set of cases ranged from 0.806 to 0.882. In the case of the two separate datasets, utilizing the proposed approach we achieved areas under the curve of around 0.890. Conclusions The concept of the matching layer is generalizable and can be used to improve the overall performance of the transfer learning techniques using deep convolutional neural networks. When fully developed as a clinical tool, the methods proposed in this paper have the potential to help radiologists with breast mass classification in ultrasound.

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Section: Methodsmentioning

confidence: 99%

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

et al. 2019

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“…Examples of methods of quantitative analysis of raw ultrasonic signals have been described in detail by several authors in previous papers . Here, we provide two simple Matlab data‐processing examples which may help other users to manipulate the data.…”

Section: Data Format and Usage Notesmentioning

confidence: 99%

“…The OASBUD were previously used by our group to assess the statistical properties of ultrasound echoes and differentiate breast lesions. We have shown that the use of the homodyned K distribution may be helpful in differentiation of lesion type, and that using the combination of the Nakagami distribution and the BI‐RADS may improve the specificity of traditional ultrasonic examination …”

Section: Introductionmentioning

confidence: 99%

Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions

Piotrzkowska-Wróblewska

Dobruch‐Sobczak

Byra

et al. 2017

Medical Physics

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“…Up to now, the most reliable diagnostic results were obtained for liver and renal tissues lesions, because their normal, healthy structures are nearly homogeneous. The classification of the strongly heterogeneous breast tissue is however still an open issue (Byra et al, 2016). Recent study revealed that the medium contraction and expansion induced by a temperature change results in relative displacement of the scatterers in heated tissue.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Measurement of Temperature Rise in Breast Cyst and in Neighbouring Tissues as a Method of Tissue Differentiation

Gambin

Byra

Kruglenko

et al. 2016

Archives of Acoustics

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Texture of ultrasound images contain information about the properties of examined tissues. The analysis of statistical properties of backscattered ultrasonic echoes has been recently successfully applied to differentiate healthy breast tissue from the benign and malignant lesions. We propose a novel procedure of tissue characterization based on acquiring backscattered echoes from the heated breast. We have proved that the temperature increase inside the breast modifies the intensity, spectrum of the backscattered signals and the probability density function of envelope samples. We discuss the differences in probability density functions in two types of tissue regions, e.g. cysts and the surrounding glandular tissue regions. Independently, Pennes bioheat equation in heterogeneous breast tissue was used to describe the heating process. We applied the finite element method to solve this equation. Results have been compared with the ultrasonic predictions of the temperature distribution. The results confirm the possibility of distinguishing the differences in thermal and acoustical properties of breast cyst and surrounding glandular tissues.

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Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters

Abstract: The study showed that the analysis of internal changes in lesion parametric maps leads to a better classification of breast tumors. The authors recommend combining multiple features for characterization, instead of using only one parameter, especially in the case of heterogeneous lesions.

Cited by 53 publications

References 57 publications

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions

Ultrasonic Measurement of Temperature Rise in Breast Cyst and in Neighbouring Tissues as a Method of Tissue Differentiation

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