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1. Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI

   https://doi.org/10.3390/diagnostics12010123  

   Almajalid, Rania ; Zhang, Ming ; Shan, Juan ( January 2022 , Diagnostics)

   In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model’s performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting allmore »the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset.« less
2. Using a Human Liver Tissue Equivalent (hLTE) Platform to Define the Functional Impact of Liver-Directed AAV Gene Therapy

   Ramamurthy, R ; Zheng, WT ; George, S ; Wan, M ; Zhou, Y ; Lu, B ; Bishop, C ; Atala, A ; Porada, C ; Almeida-Porada, G ( December 2021 , ASH)

   2938 Using a Human Liver Tissue Equivalent (hLTE) Platform to Define the Functional Impact of Liver-Directed AAV Gene Therapy 63rd ASH Annual Meeting and Exposition, December 11-14, 2021, Georgia World Congress Center, Atlanta, GA Program: Oral and Poster Abstracts Session: 801. Gene Therapies: Poster II Hematology Disease Topics & Pathways: Bleeding and Clotting, Biological, Translational Research, Hemophilia, Genetic Disorders, Clinically Relevant, Diseases, Gene Therapy, Therapies Sunday, December 12, 2021, 6:00 PM-8:00 PM Ritu M Ramamurthy1*, Wen Ting Zheng2*, Sunil George, PhD1*, Meimei Wan1*, Yu Zhou, PhD1*, Baisong Lu, PhD1*, Colin E Bishop, PhD1*, Anthony Atala, M.D.1*, Christopher D Porada, PhD1* and M. Graca Almeida-Porada, MD3 1Fetal Research and Therapy Program, Wake Forest Institute for Regenerative Medicine, Winston Salem, NC 2Massachusetts Institute of Technology, Cambridge, MA 3Fetal Research and Therapy Program, Wake Forest Institute For Regenerative Medicine, Winston-Salem, NC Clinical trials employing AAV vectors for hemophilia A have been hindered by unanticipated immunological and/or inflammatory responses in some of the patients. Also, these trials have often yielded lower levels of transgene expression than were expected based upon preclinical studies, highlighting the poor correlation between the transduction efficiency observed in traditional 2D cultures of primary cells in vitro, and that observed inmore »those same cell types in vivo. It has been also recognized that there are marked species-specific differences in AAV-vector tropism, raising the critical question of the accuracy with which various animal models will likely predict tropism/vector transduction efficiency, and eventual treatment success in humans. Human liver tissue equivalents (hLTEs) are comprised of major cell types in the liver in physiologically relevant frequencies and possess the ability to recapitulate the biology and function of native human liver. Here, we hypothesize that hLTEs can be used as a better model to predict the efficacy and safety of AAV gene therapy in humans. We fabricated hLTEs using 75% hepatocytes, 10% stellate cells, 10% Kupffer cells, and 5% liver sinusoid-derived endothelial cells in 96-well Elplasia plates with 79 microwells per well. hLTEs were transduced at an MOI of 105vg/cell, on the day of fabrication, with the clinically relevant serotypes AAV5 (hLTE-5) or AAV3b (hLTE-3b), both encoding a GFP reporter. After 4 days of self-aggregation, live/dead assay was performed to confirm viability. Non-transduced hLTEs served as negative controls (hLTE(-)), and hLTEs exposed to 20 mM acetaminophen were used as positive controls for liver inflammation/damage. Incucyte® Live-Cell Imaging system was used to track the aggregation and GFP expression of hLTEs. Over the course of the next 5 days, media was collected to determine hepatic functionality, RNA was isolated to assess dysregulation of genes involved in inflammation and fibrosis, DNA was isolated to determine whether AAV vectors integrate into the genome of human hepatocytes and, if so, to define the frequency at which this occurs and the genomic loci of integration, and hLTEs were fixed and processed at appropriate times for histological analyses and transmission electron microscopy (TEM). TEM analysis revealed that all groups exhibited microvilli and bile-canaliculus-like structures, demonstrating the formation of a rudimentary biliary system and, more importantly, proving that hLTEs resemble native liver structure. Incucyte® imaging showed that AAV5 and AAV3b transduction impaired formation of hLTEs (57.57 ± 2.42 and 24.57 ± 4.01 spheroids/well, respectively) in comparison with hLTE(-) (74.86 ± 3.8 spheroids/well). Quantification of GFP expression demonstrated that AAV5 yielded the most efficient transduction of hLTEs (fold change in GFP expression compared to control: 2.73 ± 0.09 and 1.19 ± 0.03 for hLTE-5 and hLTE-3b, respectively). Chromogenic assays showed decreased urea production in cell culture supernatants of AAV transduced groups compared to the non-transduced hLTEs on days 6 and 10 of culture, demonstrating decreased hepatocyte functionality. However, ALT and AST levels were similar in all groups. On day 10, hLTEs were either used for RNA isolation or fixed in 4% PFA and processed for histology. Masson’s Trichrome and Alcian Blue/Sirius Red staining was performed to detect fibrosis, which was then quantified using ImageJ. These analyses showed no significant increase in fibrosis in either hLTE-5 or hLTE-3b compared to hLTE(-). Nevertheless, RT2 PCR Array for Human Fibrosis detected dysregulation of several genes involved in fibrosis/inflammation in both hLTE-5 and hLTE-3b (16/84 and 26/84, respectively). In conclusion, data collected thus far show successful recapitulation of native liver biology and demonstrate that AAV5 transduces hLTEs more efficiently than AAV3b. However, impaired self-aggregation and decreased hepatocyte functionality was observed in both AAV-transduced groups. Studies to address the incidence and location(s) of AAV integration are ongoing. We have thus shown that the hLTE system can provide critical new knowledge regarding the efficacy and safety of AAV gene therapy in the human liver. Disclosures: No relevant conflicts of interest to declare.« less
3. A comparison of diceCT and histology for determination of nasal epithelial type

   https://doi.org/10.7717/peerj.12261  

   Smith, Timothy D. ; Corbin, Hayley M. ; King, Scot E. ; Bhatnagar, Kunwar P. ; DeLeon, Valerie B. ( January 2021 , PeerJ)

   Diffusible iodine-based contrast-enhanced computed tomography (diceCT) has emerged as a viable tool for discriminating soft tissues in serial CT slices, which can then be used for three-dimensional analysis. This technique has some potential to supplant histology as a tool for identification of body tissues. Here, we studied the head of an adult fruit bat ( Cynopterus sphinx ) and a late fetal vampire bat ( Desmodus rotundus ) using diceCT and µCT. Subsequently, we decalcified, serially sectioned and stained the same heads. The two CT volumes were rotated so that the sectional plane of the slice series closely matched that of histological sections, yielding the ideal opportunity to relate CT observations to corresponding histology. Olfactory epithelium is typically thicker, on average, than respiratory epithelium in both bats. Thus, one investigator (SK), blind to the histological sections, examined the diceCT slice series for both bats and annotated changes in thickness of epithelium on the first ethmoturbinal (ET I), the roof of the nasal fossa, and the nasal septum. A second trial was conducted with an added criterion: radioopacity of the lamina propria as an indicator of Bowman’s glands. Then, a second investigator (TS) annotated images of matching histological sections based onmore »microscopic observation of epithelial type, and transferred these annotations to matching CT slices. Measurements of slices annotated according to changes in epithelial thickness alone closely track measurements of slices based on histologically-informed annotations; matching histological sections confirm blind annotations were effective based on epithelial thickness alone, except for a patch of unusually thick non-OE, mistaken for OE in one of the specimens. When characteristics of the lamina propria were added in the second trial, the blind annotations excluded the thick non-OE. Moreover, in the fetal bat the use of evidence for Bowman’s glands improved detection of olfactory mucosa, perhaps because the epithelium itself was thin enough at its margins to escape detection. We conclude that diceCT can by itself be highly effective in identifying distribution of OE, especially where observations are confirmed by histology from at least one specimen of the species. Our findings also establish that iodine staining, followed by stain removal, does not interfere with subsequent histological staining of the same specimen.« less
4. Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning–Assisted Gland Analysis

   https://doi.org/10.1158/0008-5472.CAN-21-2843  

   Xie, Weisi ; Reder, Nicholas P. ; Koyuncu, Can ; Leo, Patrick ; Hawley, Sarah ; Huang, Hongyi ; Mao, Chenyi ; Postupna, Nadia ; Kang, Soyoung ; Serafin, Robert ; et al ( December 2021 , Cancer Research)

   An end-to-end pipeline for deep learning–assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

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5. 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 skullmore »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.« less