Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Breast lesion detection using ultrasound imaging is considered an important step of Computer-Aided Diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e. Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.authorsversionPeer reviewe

Sistema digital para realizar biopsia estereotáxica

Referencia OEPM: P200401878 .-- Fecha de solicitud: 29/07/2004.-- Titulares: Udiat Centre Diagnostic, S.A., Institut de Fisica d'Altes Enerties (IFAE), Consejo Superior de Investigaciones Científicas (CSIC).Sistema digital para realizar biopsias estereotáxicas con una aguja de biopsia, comprendiendo dicho sistema una serie de dispositivos para: emitir rayos X, detectar y transformar fotones de rayos X en señales eléctricas, posicionar una muestra de tejido entre la fuente de rayos X y el detector, procesar las señales eléctricas y generar imágenes. El sistema bien puede disponer de una serie de dispositivos complementarios a los mencionados, bien puede disponer de unos medios de posicionamiento para situar en dos posiciones los dispositivos mencionados y obtener imágenes según dos orientaciones.Peer reviewe

Sistema digital para realizar biopsia esterotáxica.

Filing Date: 2005-07-27.--Priority Data: ES P200401878 (2004-07-29)The invention relates to a digital system (1) for performing stereotaxic biopsies with a biopsy needle. The inventive system (1) comprises a series of devices which are used to: emit X-rays, detect and transform X-ray photons into electric signals, position a tissue sample between the X-ray source and the detector, process the electric signals, and generate images. The system can also be equipped with a series of devices complementary to those mentioned above, as well as a means for positioning the aforementioned devices in two positions and obtaining images in two different orientations

Feasibility of depth sensors to study breast deformation during mammography procedures

Virtual clinical trials (VCT) currently represent key tools for breast imaging optimisation, especially in two-dimensional planar mammography and digital breast tomosynthesis. Voxelised breast models are a crucial part of VCT as they allow the generation of synthetic image projections of breast tissue distribution. Therefore, realistic breast models containing an accurate representation of women breasts are needed. Current voxelised breast models show, in their compressed version, a very round contour which might not be representative of the entire population. This work pretends to develop an imaging framework, based on depth cameras, to investigate breast deformation during mammographic compression. Preliminary results show the feasibility of depth sensors for such task, however post-processing steps are needed to smooth the models. The proposed framework can be used in the future to produce more accurate compressed breast models, which will eventually generate more realistic images in VCTThis work is part of the SCARtool project (H2020-MSCA-IF-2014, reference 657875), a research funded by the European Union within the Marie Sklodowska-Curie Innovative Training Networks. Also, some of the authors have been partially supported from the Ministry of Economy and Competitiveness of Spain, under project references TIN2012-37171-C02-01 and DPI2015-68442-R, and the FPI grant BES-2013-06531