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1. Comparisons between Two-Dimensional and Three-Dimensional Fabric Characterizations Based on Scalar Parameters for Sands

   https://doi.org/10.1061/9780784482803.001  

   Sun, Quan ; Zheng, Junxing ( February 2020 , Geo-Congress 2020)

   Hambleton, J. P. (Ed.)

   Soil particles that have been deposited through water or air generally align their largest projected surface area normal to the depositional direction, which generates a cross-anisotropic fabric of granular soils. Researchers have used both two-dimensional (2D) and three-dimensional (3D) images to determine scalar fabric parameters of granular soils, including void ratio, coordination number, and average branch vector length. This study aims to evaluate the accuracy and effectiveness of 2D images to characterize fabric in 3D soils based on scalar parameters. The X-ray computed tomography (X-ray CT) is used to reconstruct the 3D volumetric images of three air-pluviated sand specimens, including crushed limestone, Griffin sand, and glass beads. Then, six slices are obtained by vertically cutting the 3D volumetric image in an angle increment of 30 degrees. The 3D and 2D images are analyzed to determine scalar fabric parameters. The results show that coordination numbers and average branch vector lengths computed from 2D images underestimate these values in 3D granular soils. The void ratios computed from 2D images vary a large range depending on slicing directions, which cannot provide reliable fabric characterizations for 3D granular soils. 

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2. https://doi.org/10.1061/9780784482803.017

   https://doi.org/10.1061/9780784482803.017  

   Ahmed, Sharif A ; Martinez, Alejandro ( February 2020 , GeoCongress 2020)

   This paper presents the prospect of 3D printing technology to generate artificial soil analogs with the goal of modeling the mechanical behavior of coarse-grained soils. 3D X-ray CT scans of natural angular and rounded sand particles have been used to generate angular and rounded particle analogs using the polyjet 3D printing technology. A comparison of the scanned natural sand particles and the 3D printed particles demonstrates the ability of 3D printing technology to reproduce the shape and size of the sand particles. The results of oedometer compression tests on the angular and rounded natural and 3D printed particles are used to demonstrate the effect of constituent material (i.e. quartz versus polymer) stiffness on the measured soil compressibility and investigate the normalization of the response using the Hertz contact theory. The results provided in this paper also include comparison of the small-strain moduli–mean effective stress relationship obtained for the natural and 3D printed soils. This paper illustrates the potential use of 3D printed analogs to model the mechanical behavior of coarse-grained soils and identifies future research needs for implementation of the proposed normalization scheme within the critical state soil mechanics framework. 

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3. In vivo ultrasound-switchable fluorescence imaging using a camera-based system

   https://doi.org/10.1364/BOE.385996  

   Yu, Shuai ; Yao, Tingfeng ; Liu, Yang ; Yuan, Baohong ( February 2020 , Biomedical Optics Express)

   Ultrasound-switchable fluorescence (USF) is a novel imaging technique that provides high spatial resolution fluorescence images in centimeter-deep biological tissue. Recently, we successfully demonstrated the feasibility ofin vivoUSF imaging using a frequency-domain photomultiplier tube-based system. In this work, for the first time we carried outin vivoUSF imaging via a camera-based USF imaging system. The system acquires a USF signal on a two-dimensional (2D) plane, which facilitates the image acquisition because the USF scanning area can be planned based on the 2D image and provides high USF photon collection efficiency. We demonstratedin vivoUSF imaging in the mouse’s glioblastoma tumor with multiple targets via local injection. In addition, we designed the USF contrast agents with different particle sizes (70 nm and 330 nm) so that they could bio-distribute to various organs (spleen, liver, and kidney) via intravenous (IV) injections. The results showed that the contrast agents retained stable USF properties in tumors and some organs (spleen and liver). We successfully achievedin vivoUSF imaging of the mouse’s spleen and liver via IV injections. The USF imaging results were compared with the images acquired from a commercial X-ray micro computed tomography (micro-CT) system.

    

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4. Multi-Series CT Image Super-Resolution by using Transfer Generative Adversarial Network

   Xiao, Yao ; Arreola, Manuel M. ; Barreto, Izabella ; Bolch, Wesley E. ; Fox, W. Christopher ; Peters, Keith ; Rajderkar, Dhanashree A. ; Rees, John H. ; Fang, Ruogu ( June 2020 , Society for Imaging Informatics in Medicine (SIIM) Annual Meeting)

   Introduction Multi-series CT (MSCT) scans, including non-contrast CT (NCCT), CT Perfusion (CTP), and CT Angiography (CTA), are widely used in acute stroke imaging. While each scan has its advantage in disease diagnosis, the varying image resolution of different series hinders the ability of the radiologist to discern subtle suspicious findings. Besides, higher image quality requires high radiation doses, leading to increases in health risks such as cataract formation and cancer induction. Thus, it is highly crucial to develop an approach to improve MSCT resolution and to lower radiation exposure. Hypothesis MSCT imaging of the same patient is highly correlated in structural features, the transferring and integration of the shared and complementary information from different series are beneficial for achieving high image quality. Methods We propose TL-GAN, a learning-based method by using Transfer Learning (TL) and Generative Adversarial Network (GAN) to reconstruct high-quality diagnostic images. Our TL-GAN method is evaluated on 4,382 images collected from nine patients’ MSCT scans, including 415 NCCT slices, 3,696 CTP slices, and 271 CTA slices. We randomly split the nine patients into a training set (4 patients), a validation set (2 patients), and a testing set (3 patients). In preprocessing, we remove the background and skull and visualize in brain window. The low-resolution images (1/4 of the original spatial size) are simulated by bicubic down-sampling. For training without TL, we train different series individually, and for with TL, we follow the scanning sequence (NCCT, CTP, and CTA) by finetuning. Results The performance of TL-GAN is evaluated by the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) index on 184 NCCT, 882 CTP, and 107 CTA test images. Figure 1 provides both visual (a-c) and quantity (d-f) comparisons. Through TL-GAN, there is a significant improvement with TL than without TL (training from scratch) for NCCT, CTP, and CTA images, respectively. These significances of performance improvement are evaluated by one-tailed paired t-tests (p < 0.05). We enlarge the regions of interest for detail visual comparisons. Further, we evaluate the CTP performance by calculating the perfusion maps, including cerebral blood flow (CBF) and cerebral blood volume (CBV). The visual comparison of the perfusion maps in Figure 2 demonstrate that TL-GAN is beneficial for achieving high diagnostic image quality, which are comparable to the ground truth images for both CBF and CBV maps. Conclusion Utilizing TL-GAN can effectively improve the image resolution for MSCT, provides radiologists more image details for suspicious findings, which is a practical solution for MSCT image quality enhancement. 

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5. Recent Advances in the Temple University Digital Pathology Corpus

   https://doi.org/10.1109/SPMB47826.2019.9037859  

   Hunt, I. ; Husain, S. ; Simon, J. ; Obeid, I. ; Picone, J. ( December 2019 , IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad ; Picone, Joseph ; Selesnick, Ivan (Ed.)

   The Neural Engineering Data Consortium (NEDC) is developing a large open source database of high-resolution digital pathology images known as the Temple University Digital Pathology Corpus (TUDP) [1]. Our long-term goal is to release one million images. We expect to release the first 100,000 image corpus by December 2020. The data is being acquired at the Department of Pathology at Temple University Hospital (TUH) using a Leica Biosystems Aperio AT2 scanner [2] and consists entirely of clinical pathology images. More information about the data and the project can be found in Shawki et al. [3]. We currently have a National Science Foundation (NSF) planning grant [4] to explore how best the community can leverage this resource. One goal of this poster presentation is to stimulate community-wide discussions about this project and determine how this valuable resource can best meet the needs of the public. The computing infrastructure required to support this database is extensive [5] and includes two HIPAA-secure computer networks, dual petabyte file servers, and Aperio’s eSlide Manager (eSM) software [6]. We currently have digitized over 50,000 slides from 2,846 patients and 2,942 clinical cases. There is an average of 12.4 slides per patient and 10.5 slides per case with one report per case. The data is organized by tissue type as shown below: Filenames: tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_0a001_00123456_lvl0001_s000.svs tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_00123456.docx Explanation: tudp: root directory of the corpus v1.0.0: version number of the release svs: the image data type gastro: the type of tissue 000001: six-digit sequence number used to control directory complexity 00123456: 8-digit patient MRN 2015_03_05: the date the specimen was captured 0s15_12345: the clinical case name 0s15_12345_0a001_00123456_lvl0001_s000.svs: the actual image filename consisting of a repeat of the case name, a site code (e.g., 0a001), the type and depth of the cut (e.g., lvl0001) and a token number (e.g., s000) 0s15_12345_00123456.docx: the filename for the corresponding case report We currently recognize fifteen tissue types in the first installment of the corpus. The raw image data is stored in Aperio’s “.svs” format, which is a multi-layered compressed JPEG format [3,7]. Pathology reports containing a summary of how a pathologist interpreted the slide are also provided in a flat text file format. A more complete summary of the demographics of this pilot corpus will be presented at the conference. Another goal of this poster presentation is to share our experiences with the larger community since many of these details have not been adequately documented in scientific publications. There are quite a few obstacles in collecting this data that have slowed down the process and need to be discussed publicly. Our backlog of slides dates back to 1997, meaning there are a lot that need to be sifted through and discarded for peeling or cracking. Additionally, during scanning a slide can get stuck, stalling a scan session for hours, resulting in a significant loss of productivity. Over the past two years, we have accumulated significant experience with how to scan a diverse inventory of slides using the Aperio AT2 high-volume scanner. We have been working closely with the vendor to resolve many problems associated with the use of this scanner for research purposes. This scanning project began in January of 2018 when the scanner was first installed. The scanning process was slow at first since there was a learning curve with how the scanner worked and how to obtain samples from the hospital. From its start date until May of 2019 ~20,000 slides we scanned. In the past 6 months from May to November we have tripled that number and how hold ~60,000 slides in our database. This dramatic increase in productivity was due to additional undergraduate staff members and an emphasis on efficient workflow. The Aperio AT2 scans 400 slides a day, requiring at least eight hours of scan time. The efficiency of these scans can vary greatly. When our team first started, approximately 5% of slides failed the scanning process due to focal point errors. We have been able to reduce that to 1% through a variety of means: (1) best practices regarding daily and monthly recalibrations, (2) tweaking the software such as the tissue finder parameter settings, and (3) experience with how to clean and prep slides so they scan properly. Nevertheless, this is not a completely automated process, making it very difficult to reach our production targets. With a staff of three undergraduate workers spending a total of 30 hours per week, we find it difficult to scan more than 2,000 slides per week using a single scanner (400 slides per night x 5 nights per week). The main limitation in achieving this level of production is the lack of a completely automated scanning process, it takes a couple of hours to sort, clean and load slides. We have streamlined all other aspects of the workflow required to database the scanned slides so that there are no additional bottlenecks. To bridge the gap between hospital operations and research, we are using Aperio’s eSM software. Our goal is to provide pathologists access to high quality digital images of their patients’ slides. eSM is a secure website that holds the images with their metadata labels, patient report, and path to where the image is located on our file server. Although eSM includes significant infrastructure to import slides into the database using barcodes, TUH does not currently support barcode use. Therefore, we manage the data using a mixture of Python scripts and manual import functions available in eSM. The database and associated tools are based on proprietary formats developed by Aperio, making this another important point of community-wide discussion on how best to disseminate such information. Our near-term goal for the TUDP Corpus is to release 100,000 slides by December 2020. We hope to continue data collection over the next decade until we reach one million slides. We are creating two pilot corpora using the first 50,000 slides we have collected. The first corpus consists of 500 slides with a marker stain and another 500 without it. This set was designed to let people debug their basic deep learning processing flow on these high-resolution images. We discuss our preliminary experiments on this corpus and the challenges in processing these high-resolution images using deep learning in [3]. We are able to achieve a mean sensitivity of 99.0% for slides with pen marks, and 98.9% for slides without marks, using a multistage deep learning algorithm. While this dataset was very useful in initial debugging, we are in the midst of creating a new, more challenging pilot corpus using actual tissue samples annotated by experts. The task will be to detect ductal carcinoma (DCIS) or invasive breast cancer tissue. There will be approximately 1,000 images per class in this corpus. Based on the number of features annotated, we can train on a two class problem of DCIS or benign, or increase the difficulty by increasing the classes to include DCIS, benign, stroma, pink tissue, non-neoplastic etc. Those interested in the corpus or in participating in community-wide discussions should join our listserv, nedc_tuh_dpath@googlegroups.com, to be kept informed of the latest developments in this project. You can learn more from our project website: https://www.isip.piconepress.com/projects/nsf_dpath. 

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