USCT data challenge

Abstract

In the past years we have perceived within the USCT research community a demand for freely available USCT data sets. Inspired by the idea of Open Science, this collection of data sets could stimulate the collaboration and the exchange of ideas and experiences between USCT researchers. In addition, it may lead to comprehensive comparison of different reconstruction algorithms and their results. Finally, by collecting feedback from the users about data and system architecture, valuable information is gathered for further development of measurement setups. For the above reasons, we have initiated a digital portal with several reference data sets and access scripts under free licenses. To kick off this initiative, we organized a USCT data challenge event at SPIE Medical Imaging 2017.

Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography

“…The first phantom, in the following referred to as MUST phantom, is derived from an MRI 2D coronal slice through a breast, which data were published as USCT Data Challenge 2019. The second data set uses the MUBI/CSIC data set donated as part of the USCT Data Challenge 2017, 21 which was obtained from lab measurements of a gelatin-based synthetic phantom with a diameter of 94 mm, two inclusions with a diameter of 20 mm and two steel needles with a diameter of 0.25 mm, although we do not expect to resolve the needles with transmission tomography.…”

“…The algorithms are tested on simulated and measured data obtained from synthetic phantom scans of the previous USCT Data Challenges. 21…”

Deep learning based neural network application for automatic ultrasonic computed tomographic bone image segmentation

“…Accordingly, it can extract extraction of different anatomical structures, to ensure the automatic segmentation of the regions of interest [24]. Nowadays, the Ultrasonic Computed Tomography (USCT) device, an existing new technique, has revolutionized X-rays and ultrasonic imaging [28]. It is a non-invasive and non-ionizing technique.…”

“…Our work aims to carry out Convolutional Neural Network (CNN) learning with VGG-SegNet and VGG-Unet models applied on a USCT image dataset, in order to achieve an automatic segmentation of the region. Thus, we improve a Variable Structure Model of neurons (VSMN) [3] and apply it on medical images to get a significant increase in data, given the problem of unavailability of USCT images [28].…”

Deep Learning based Approach for Bone Diagnosis Classification in Ultrasonic Computed Tomographic Images

“…Our approach in this paper consists of a data augmentation of the number of USCT images to achieve a big data augmentation by pre-processing algorithms. Thus we solve the issue of USCT data unvailibility [16]. Then, a transfer deep learning models have been done such as Convolutional Neural Networks models (Inception V3, MobileNet-v2 and Evolutionnary Neural Network model such as AmoebaNet are applied on our dataset.…”