KEYWORDS: Breast, Monte Carlo methods, Education and training, Muscles, Polymethylmethacrylate, Lead, Convolutional neural networks, Chest, Digital breast tomosynthesis, Picosecond phenomena
PurposeScatter radiation in contrast-enhanced digital breast tomosynthesis (CEDBT) reduces the image quality and iodinated lesion contrast. Monte Carlo simulation can provide accurate scatter estimation at the cost of computational burden. A model-based convolutional method trades off accuracy for processing speed. The purpose of this study is to develop a fast and robust deep-learning (DL) convolutional neural network (CNN)-based scatter correction method for CEDBT.ApproachProjection images and scatter maps of digital anthropomorphic breast phantoms were generated using Monte Carlo simulations. Experiments were conducted to validate the simulated scatter-to-primary ratio (SPR) at different locations of a breast phantom. Simulated projection images were used for CNN training and testing. Two separate U-Nets [low-energy (LE)-CNN and high-energy (HE)-CNN] were trained for LE and HE spectrum, respectively. CNN-based scatter correction was applied to a clinical case with a malignant iodinated mass to evaluate the influence on the lesion detection.ResultsThe average and standard deviation of mean absolute percentage error of LE-CNN and HE-CNN estimated scatter map are 2 % ± 0.4 % and 2.4 % ± 0.8 % , respectively. For clinical cases, the lesion signal difference to noise ratio average improvement was 190% after CNN-based scatter correction. To conduct scatter correction on clinical CEDBT images, the whole process of loading CNNs parameters and scatter correction for LE and HE images took <4 s, with 9 GB GPU computational cost. The SPR variation across the breast agrees between experimental measurements and Monte Carlo simulations.ConclusionsWe developed a CNN-based scatter correction method for CEDBT in both CC view and mediolateral-oblique view with high accuracy and fast speed.
Fracture fixation surgeries require a careful and well thought out surgical plan, mainly due to the wide range of possibilities in the fracture types and available choices in fixation constructs. There is considerable interest in virtual 3D planning tools ranging from 3D visualization, interactive fracture reduction and bio-mechanical analysis of fracture fixation construct stability to arrive at optimal plan. One of the key steps prior to reconstructing 3D fractures is accurate fracture segmentation which can be tedious and time consuming even with semi-automated tools. In this paper, we report preliminary results from our attempt to fully automate the segmentation of fractured bone using deep learning. We performed experiments using the widely used 3D segmentation model called 3D U-Net on a dataset of 14 CT volumes. The dataset is randomly divided into train, validation and test splits comprising 7, 3 and 4 volumes respectively. Even with a small training set of femur fractures, we were able to achieve a mean dice score of 0.861 with a mean sensitivity of 0.899. The model was able to capture the challenging fracture regions and could cleanly separate the femur head and socket. Apart from this, we also studied the impact of different loss functions on the network’s performance. The results indicate that deep learning based segmentation methodologies have good potential in automating the challenging task of fractured femur segmentation. Further improvement is expected with a larger collection of such fractured samples
We demonstrate the application of the Thin Shell Demons (TSD) surface registration algorithm in registering the dental scans obtained from intra-oral scanners (IOS) and Cone Beam Computed Tomography (CBCT) in a semi-automatic manner. The reconstructed dentition obtained from CBCT lacks the accuracy for diagnosis and appliance fabrication that IOS provides. Current methods to register IOS to CBCT typically use Iterative Closest Point (ICP) but suffer from a lack of precision and accuracy. TSD registration has previously been shown to produce superior registration results in presence of missing patches and holes. In this work, for the first time we share its application in dental scan registration. We perform experiments on dental surface meshes obtained from CBCT and IOS for two patients who have undergone orthognathic surgery. Our method first registers the IOS mesh with the CBCT mesh using ICP. To obtain tight alignment of the tooth surface, we perform a refinement using the TSD registration. We quantify the improvement in registration by measuring the distance between the closest points present on the surface of six teeth in the IOS and CBCT meshes. Compared to using only ICP registration, TSD decreases the mean surface distance between patches and significantly improves the alignment. We also share qualitative results that clearly demonstrate the improvement due to TSD. For wide adoption and ease of access, we also share a publicly available Thin Shell Demons implementation in C++ under the open source image processing library Insight Toolkit (ITK). Python wrapping of the code is also made available. The code is available at the url: https://github.com/InsightSoftwareConsortium/ITKThinShellDemons.
The image quality of contrast-enhanced digital breast tomosynthesis (CEDBT) is degraded by scatter radiation. Scatter correction can improve the object contrast and reduce the cupping artifacts, but the image quality is limited by the increased image noise. In this study we investigate the effect of scatter correction on image noise in CEDBT. A scatter correction method based on image convolution with scatter-to-primary ratio kernel was applied. We analyzed the noise power spectrum (NPS) for CEDBT projection images before and after scatter correction using CIRS breast phantoms and evaluated the signal-difference-to-noise ratio (SDNR) of the iodine objects after image reconstruction. We applied image filtering to reduce image noise after scatter correction for phantom and clinical images. A deep learning based denoising technique was applied to further reduce the image noise for clinical images. Our results show that the scatter correction increases the image noise in dual-energy subtracted images, and the improvement in SDNR from scatter correction is limited. Noise reduction applied after scatter removal can regain the benefit in SDNR from scatter correction and further improve the visualization of contrast enhancement in CEDBT.
KEYWORDS: Breast, Monte Carlo methods, Digital breast tomosynthesis, Dual energy imaging, Point spread functions, Computer simulations, Imaging systems, Convolutional neural networks
Dual energy contrast-enhanced digital breast tomosynthesis (CEDBT) uses weighted subtraction of two energy spectra to highlight tumor angiogenesis with uptake of iodinated contrast agent. The high energy scan contains more severe scatter radiation than regular low energy DBT. The purpose of this study is to develop a convolutional neural network (CNN) based scatter correction method for dual energy CEDBT in both craniocaudal (CC) view and mediolateral oblique (MLO) view. Anthropomorphic digital breast phantoms with various glandularity and 3D shape were generated using the VICTRE software tool developed by the FDA. The pectoralis muscle layer was inserted into the phantoms for MLO view. Projection images with and without scatter radiation were simulated using Monte Carlo (MC) simulation code of VICTRE, meeting the prototype Siemens Mammomat Inspiration CEDBT system with 300 μm thick a-Se detector, 25 projections within 46-degree angular range. Scatter radiation ground truth was generated from MC simulated projection images to train CNN. Two separate U-net CNNs were trained to predict scatter radiation maps. Mean absolute percentage error (MAPE) was used as the loss function. The average MAPE of this method is less than 3 % from the ground truth of MC simulation. The proposed scatter correction method was then applied to clinical cases, demonstrating the reduction of cupping artifact and the improvement in contrast object conspicuity.
Melanoma is the most dangerous form of skin cancer that often resembles moles. Dermatologists often recommend regular skin examination to identify and eliminate Melanoma in its early stages. To facilitate this process, we propose a hand-held computer (smart-phone, Raspberry Pi) based assistant that classifies with the dermatologist-level accuracy skin lesion images into malignant and benign and works in a standalone mobile device without requiring network connectivity. In this paper, we propose and implement a hybrid approach based on advanced deep learning model and domain-specific knowledge and features that dermatologists use for the inspection purpose to improve the accuracy of classification between benign and malignant skin lesions. Here, domain-specific features include the texture of the lesion boundary, the symmetry of the mole, and the boundary characteristics of the region of interest. We also obtain standard deep features from a pre-trained network optimized for mobile devices called Google's MobileNet. The experiments conducted on ISIC 2017 skin cancer classification challenge demonstrate the effectiveness and complementary nature of these hybrid features over the standard deep features. We performed experiments with the training, testing and validation data splits provided in the competition. Our method achieved area of 0.805 under the receiver operating characteristic curve. Our ultimate goal is to extend the trained model in a commercial hand-held mobile and sensor device such as Raspberry Pi and democratize the access to preventive health care.
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