scholarly journals Empirical Assessment of Breast Lesion Detection Capability Through an Innovative Microwave Imaging Device

2021 ◽  
Author(s):  
Lorenzo Sani ◽  
Alessandro Vispa ◽  
Navid Ghavami ◽  
Riccardo Loretoni ◽  
Michele Duranti ◽  
...  
Keyword(s):  
Breast Lesion ◽  
Microwave Imaging ◽  
Lesion Detection ◽  
Detection Capability ◽  
Imaging Device ◽  
Empirical Assessment

scholarly journals Free-Space Operating Microwave Imaging Device for Bone Lesion Detection: A Phantom Investigation

2020 ◽  
Vol 19 (12) ◽  
pp. 2393-2397
Author(s):  
Banafsheh Khalesi ◽  
Behnaz Sohani ◽  
Navid Ghavami ◽  
Mohammad Ghavami ◽  
Sandra Dudley ◽  
...  
Keyword(s):  
Free Space ◽  
Bone Lesion ◽  
Microwave Imaging ◽  
Lesion Detection ◽  
Imaging Device

scholarly journals Breast lesion detection through MammoWave device: Empirical detection capability assessment of microwave images’ parameters

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250005
Author(s):  
Lorenzo Sani ◽  
Alessandro Vispa ◽  
Riccardo Loretoni ◽  
Michele Duranti ◽  
Navid Ghavami ◽  
...  
Keyword(s):  
Breast Lesion ◽  
Statistical Significance ◽  
Radiological Finding ◽  
Lesion Detection ◽  
Imaging Device ◽  
Receiver Operating Characteristic Curves ◽  
Rule Of Thumb ◽  
Huygens Principle ◽  
Breast Assessment ◽  
Selection Of

MammoWave is a microwave imaging device for breast lesions detection, which operates using two (azimuthally rotating) antennas without any matching liquid. Images, subsequently obtained by resorting to Huygens Principle, are intensity maps, representing the homogeneity of tissues’ dielectric properties. In this paper, we propose to generate, for each breast, a set of conductivity weighted microwave images by using different values of conductivity in the Huygens Principle imaging algorithm. Next, microwave images’ parameters, i.e. features, are introduced to quantify the non-homogenous behaviour of the image. We empirically verify on 103 breasts that a selection of these features may allow distinction between breasts with no radiological finding (NF) and breasts with radiological findings (WF), i.e. with lesions which may be benign or malignant. Statistical significance was set at p<0.05. We obtained single features Area Under the receiver operating characteristic Curves (AUCs) spanning from 0.65 to 0.69. In addition, an empirical rule-of-thumb allowing breast assessment is introduced using a binary score S operating on an appropriate combination of features. Performances of such rule-of-thumb are evaluated empirically, obtaining a sensitivity of 74%, which increases to 82% when considering dense breasts only.

scholarly journals Lesion Detection in Breast Tomosynthesis Using Efficient Deep Learning and Data Augmentation Techniques

2021 ◽  
Author(s):  
Loay Hassan ◽  
Mohamed Abedl-Nasser ◽  
Adel Saleh ◽  
Domenec Puig
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Lesion ◽  
Data Augmentation ◽  
Digital Breast Tomosynthesis ◽  
Lesion Detection ◽  
Detection Methods ◽  
Breast Tomosynthesis ◽  
Mammographic Images ◽  
Augmentation Techniques

Digital breast tomosynthesis (DBT) is one of the powerful breast cancer screening technologies. DBT can improve the ability of radiologists to detect breast cancer, especially in the case of dense breasts, where it beats mammography. Although many automated methods were proposed to detect breast lesions in mammographic images, very few methods were proposed for DBT due to the unavailability of enough annotated DBT images for training object detectors. In this paper, we present fully automated deep-learning breast lesion detection methods. Specifically, we study the effectiveness of two data augmentation techniques (channel replication and channel-concatenation) with five state-of-the-art deep learning detection models. Our preliminary results on a challenging publically available DBT dataset showed that the channel-concatenation data augmentation technique can significantly improve the breast lesion detection results for deep learning-based breast lesion detectors.

scholarly journals Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 277
Author(s):  
Zuzanna Anna Magnuska ◽  
Benjamin Theek ◽  
Milita Darguzyte ◽  
Moritz Palmowski ◽  
Elmar Stickeler ◽  
...  
Keyword(s):  
Breast Lesion ◽  
Characteristic Curve ◽  
Cancer Diagnostics ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
P Value ◽  
Future Design ◽  
Sensitivity Specificity ◽  
Derivatives Of

Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

Su1688 INVESTIGATION OF LESION DETECTION CAPABILITY OF COLON CAPSULE ENDOSCOPY FOR POLYPS

2019 ◽  
Vol 89 (6) ◽  
pp. AB380
Author(s):  
Kei Nakazawa ◽  
Sadaharu Nouda ◽  
Kazuki Kakimoto ◽  
Noriaki Sugawara ◽  
Minori Kubota ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
Lesion Detection ◽  
Detection Capability ◽  
Colon Capsule Endoscopy ◽  
Colon Capsule

scholarly journals A Phantom Investigation to Quantify Huygens Principle Based Microwave Imaging for Bone Lesion Detection

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1505 ◽  
Author(s):  
Banafsheh Khalesi ◽  
Behnaz Sohani ◽  
Navid Ghavami ◽  
Mohammad Ghavami ◽  
Sandra Dudley ◽  
...  
Keyword(s):  
Bone Lesion ◽  
Microwave Imaging ◽  
Lesion Detection ◽  
Linear Scale ◽  
Frequency Range ◽  
Spatial Displacement ◽  
Artefact Removal ◽  
Huygens Principle ◽  
Transmitting Antenna ◽  
The Impact

This paper demonstrates the outcomes of a feasibility study of a microwave imaging procedure based on the Huygens principle for bone lesion detection. This study has been performed using a dedicated phantom and validated through measurements in the frequency range of 1–3 GHz using one receiving and one transmitting antenna in free space. Specifically, a multilayered bone phantom, which is comprised of cortical bone and bone marrow layers, was fabricated. The identification of the lesion’s presence in different bone layers was performed on images that were derived after processing through Huygens’ principle, the S21 signals measured inside an anechoic chamber in multi-bistatic fashion. The quantification of the obtained images was carried out by introducing parameters such as the resolution and signal-to-clutter ratio (SCR). The impact of different frequencies and bandwidths (in the 1–3 GHz range) in lesion detection was investigated. The findings showed that the frequency range of 1.5–2.5 GHz offered the best resolution (1.1 cm) and SCR (2.22 on a linear scale). Subtraction between S21 obtained using two slightly displaced transmitting positions was employed to remove the artefacts; the best artefact removal was obtained when the spatial displacement was approximately of the same magnitude as the dimension of the lesion.

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