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Holistic Decomposition Convolution for Effective Semantic Segmentation of 3D MR Images
[article]
2018
arXiv
pre-print
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Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many different 2D medical image analysis tasks. In clinical practice, however, a large part of the medical imaging data available is in 3D. This has motivated the development of 3D CNNs for volumetric image segmentation in order to benefit from more spatial context. Due to GPU memory restrictions caused by moving to fully 3D, state-of-the-art methods depend on subvolume/patch processing and the size of the input
arXiv:1812.09834v1
fatcat:z4cscuuwtzhg5d3kwvqlk5gyqu
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... atch is usually small, limiting the incorporation of larger context information for a better performance. In this paper, we propose a novel Holistic Decomposition Convolution (HDC), for an effective and efficient semantic segmentation of volumetric images. HDC consists of a periodic down-shuffling operation followed by a conventional 3D convolution. HDC has the advantage of significantly reducing the size of the data for sub-sequential processing while using all the information available in the input irrespective of the down-shuffling factors. Results obtained from comprehensive experiments conducted on hip T1 MR images and intervertebral disc T2 MR images demonstrate the efficacy of the present approach.
Potential application of carbohydrate biomass in hydrometallurgy: one-pot reduction of metal oxides/salts under mild hydrothermal conditions
2022
RSC Advances
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Carbohydrate biomass can be employed as a reductant for metallic material preparation due to it possessing diverse reducing functional groups.
doi:10.1039/d2ra01493a
pmid:35919188
pmcid:PMC9297530
fatcat:fx2cyaxeingu3innqdc72n6ega
Molecular beacon-functionalized gold nanoparticles as probes in dry-reagent strip biosensor for DNA analysis
2009
Chemical Communications
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A study on hybrid oligomeric type II photoinitiator
2010
E-Polymers
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AbstractPreparing tailor-made hybrid photoinitiator (PI) for specific applications continues to gain momentum. The hybrid PIs comprising both H-abstraction chromophore and coinitiator moiety are the candidate materials with energy-saving and environment-protection in the photo-curing field. So two oligomeric photoinitiators (OMKs) containing benzophenone (BP) and aliphatic
doi:10.1515/epoly.2010.10.1.1533
fatcat:e6e2j3md7festiqbta4svltbri
Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks
[article]
2017
arXiv
pre-print
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We present a method to address the challenging problem of segmentation of lumbar vertebrae from CT images acquired with varying fields of view. Our method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting of a localization FCN and a segmentation FCN. More specifically, in the first step we train a regression 3D FCN (we call it "LocalizationNet") to find the bounding box of the lumbar region. After that, a 3D U-net like FCN (we call it "SegmentationNet") is then developed,
arXiv:1712.01509v1
fatcat:6sg5neqx7je3tniut4ixlpbswm
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... ich after training, can perform a pixel-wise multi-class segmentation to map a cropped lumber region volumetric data to its volume-wise labels. Evaluated on publicly available datasets, our method achieved an average Dice coefficient of 95.77 ± 0.81 symmetric surface distance of 0.37 ± 0.06 mm.
Deep Learning based Fully Automatic Quantification of Rotator Cuff Tears from MRI
2022
Zenodo
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Rotator cuff tears are the most common source of shoulder pain. Many factors can be considered to choose the right surgical treatment procedure. The most important factors are tear thickness (partial vs. full), tear size, tear shape, and muscle quality. The aim of this work was the fully automated quantification and classification of a full-thickness posterosuperior rotator cuff tear from MR images using a deep learning based approach. A complete new approach to automatically quantify and
doi:10.5281/zenodo.6397738
fatcat:mvbuoxqrg5hgxmfpcawt7hbpbm
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... fy a rotator cuff tear, based on the segmentation of the tear from MR images, was developed and validated. A neural network was trained to segment the rotator cuff tear from an MR image and automatic methods for calculating tear width and retraction and for classifying the tear according to pattern, extension and retraction were implemented. The accuracy of the automatic segmentation and the automated tear analysis were evaluated relative to the ground truth of manual segmentations by a clinical expert, and analyzed based on the ground truth segmentations. Variance in the manual segmentations was assessed in a interrater variability study of two clinical experts. The error of the automatic segmentation to one of the two clinical experts are meant to be in the same region. To make quantification accessible the whole pipeline was implemented in an existing webapp. The results were also evaluated clinically by intraoperative measurements of the rotator cuff tear performed on a separate dataset of six patients. The accuracy of the tear retraction calculation based on the developed automatic tear segmentation was 6.56 mm ± 6.48 mm in comparison to the interrater variability of tear retraction calculation based on manual segmentations of 3.77 mm ± 3.58 mm. These results show that an automatic quantification of a rotator cuff tear is possible. The achieved accuracies of the quantification pipeline for rotator cuff tears need to be improved to make them clinically useable. The large interrater variability of manual segmentation ba [...]
Discovery and Validation of Hypermethylated Markers for Colorectal Cancer
2016
Disease Markers
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Abbreviations
Disclosure Guodong Li is a co-first author. 2. 1 . 1 Subjects. ...
Authors' Contributions Jiufeng Wei and Guodong Li contributed equally as first authors. ...
doi:10.1155/2016/2192853
pmid:27493446
pmcid:PMC4963574
fatcat:u7grgybz5fhmpcookmrsvfqtny
MODIS Based Estimation of Forest Aboveground Biomass in China
2015
PLoS ONE
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Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventorybased timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level
doi:10.1371/journal.pone.0130143
pmid:26115195
pmcid:PMC4482713
fatcat:bnjg5lqn7zfvhbgk2e37hqx53y
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... -measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha −1 , with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y −1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y −1 . During the 2000s, the forests in China sequestered C by 61.9 Tg C y −1 , and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO 2 concentration, N deposition, and growth of young forests.
Analysis of Facial Images across Age Progression by Humans
2012
ISRN Machine Vision
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The appearance of human faces can undergo large variations over aging progress. Analysis of facial image taken over age progression recently attracts increasing attentions in computer-vision community. Human abilities for such analysis are, however, less studied. In this paper, we conduct a thorough study of human ability on two tasks, face verification and age estimation, for facial images taken at different ages. Detailed and rigorous experimental analysis is provided, which helps
doi:10.5402/2012/505974
fatcat:r4kucmgdjzb4dmzykhe4ycllum
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... g roles of different factors including age group, age gap, race, and gender. In addition, our study also leads to an interesting observation: for age estimation, photos from adults are more challenging than that from young people. We expect the study to provide a reference for machine-based solutions.
A new mechanical and magnetic coupling model of magnetic memory
2021
Vibroengineering PROCEDIA
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SHUJUN LIU, QIWEI YONG, DEAN HE, YONGGANG ZUO, ZHEN ZHANG, GUODONG ZENG ...
SHUJUN LIU, QIWEI YONG, DEAN HE, YONGGANG ZUO, ZHEN ZHANG, GUODONG ZENG 102 VIBROENGINEERING PROCEDIA. JUNE 2021, VOLUME 38
Fig. 2 .Fig. 4 . 24 MTS810 hydraulic servo testing machine Fig. 3. ...
doi:10.21595/vp.2021.22092
fatcat:t5k462uvpbfcjl7lsvyqiyvu54
Design of photoacoustic microscope system based on labVIEW platform
2020
MATEC Web of Conferences
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A photoacoustic microscope system based on virtual instrument development environment is presented, including ultrasonic sensor, digital oscilloscope, laser diode, personal calculation and other hardware platforms. and developed supporting software and image reconstruction algorithms. In the subcutaneous angiography experiment, the distribution characteristics of the ear blood vessels in mouse were completely reproduced perfectly, and the spatial resolution of the system can reach 14um. The
doi:10.1051/matecconf/202030904016
fatcat:qair3ddhdvb37dtysxokqky2kq
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... em and method can potentially to develop into a non-invasive biological tissue structure and functional imaging technique.
A Noise-level-aware Framework for PET Image Denoising
[article]
2022
arXiv
pre-print
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In PET, the amount of relative (signal-dependent) noise present in different body regions can be significantly different and is inherently related to the number of counts present in that region. The number of counts in a region depends, in principle and among other factors, on the total administered activity, scanner sensitivity, image acquisition duration, radiopharmaceutical tracer uptake in the region, and patient local body morphometry surrounding the region. In theory, less amount of
arXiv:2203.08034v1
fatcat:2dm6q23yjjatxafiy2roux344q
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... ing operations is needed to denoise a high-count (low relative noise) image than images a low-count (high relative noise) image, and vice versa. The current deep-learning-based methods for PET image denoising are predominantly trained on image appearance only and have no special treatment for images of different noise levels. Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only. To this end, we propose a noise-level-aware framework denoising framework that allows embedding of local noise level into a DCNN. The proposed is trained and tested on 30 and 15 patient PET images acquired on a GE Discovery MI PET/CT system. Our experiments showed that the increases in both PSNR and SSIM from our backbone network with relative noise level embedding (NLE) versus the same network without NLE were statistically significant with p<0.001, and the proposed method significantly outperformed a strong baseline method by a large margin.
Dispensed Transformer Network for Unsupervised Domain Adaptation
[article]
2021
arXiv
pre-print
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Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth labels for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised
arXiv:2110.14944v1
fatcat:khwdtqdxanftnka64q563ibl74
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... ain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques.
Aptamer−Nanoparticle Strip Biosensor for Sensitive Detection of Cancer Cells
2009
Analytical Chemistry
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We report an aptamer-nanoparticle strip biosensor (ANSB) for the rapid, specific, sensitive and lowcost detection of circulating cancer cells. Known for their high specificity and affinity, aptamers were first selected from live cells by the cell-SELEX (systematic evolution of ligands by exponential enrichment) process. When next combined with the unique optical properties of gold nanoparticles (Au-NPs), ANSBs were prepared on a lateral flow device. Ramos cells were used as a model target cell
doi:10.1021/ac901889s
pmid:19904989
pmcid:PMC2814445
fatcat:vjjsepgtxjashcbzw3jaddxxky
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... o demonstrate proof of principle. Under optimal conditions, the ANSB was capable of detecting a minimum of 4000 Ramos cells without instrumentation (visual judgment) and 800 Ramos cells with a portable strip reader within 15 minutes. Importantly, ANSB has successfully detected Ramos cells in human blood, thus providing a rapid, sensitive and low-cost quantitative tool for the detection of circulating cancer cells. ANSB therefore shows great promise for in-field and point-of-care cancer diagnosis and therapy.
An integrative multi-platform analysis for discovering biomarkers of osteosarcoma
2009
BMC Cancer
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SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry) has become an attractive approach for cancer biomarker discovery due to its ability to resolve low mass proteins and high-throughput capability. However, the analytes from mass spectrometry are described only by their mass-to-charge ratio (m/z) values without further identification and annotation. To discover potential biomarkers for early diagnosis of osteosarcoma, we designed an integrative workflow
doi:10.1186/1471-2407-9-150
pmid:19445706
pmcid:PMC2691408
fatcat:qqbnr35f25g2xan2nepy5yom4y
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... mbining data sets from both SELDI-TOF-MS and gene microarray analysis. Methods: After extracting the information for potential biomarkers from SELDI data and microarray analysis, their associations were further inferred by link-test to identify biomarkers that could likely be used for diagnosis. Immuno-blot analysis was then performed to examine whether the expression of the putative biomarkers were indeed altered in serum from patients with osteosarcoma. Results: Six differentially expressed protein peaks with strong statistical significances were detected by SELDI-TOF-MS. Four of the proteins were up-regulated and two of them were downregulated. Microarray analysis showed that, compared with an osteoblastic cell line, the expression of 653 genes was changed more than 2 folds in three osteosarcoma cell lines. While expression of 310 genes was increased, expression of the other 343 genes was decreased. The two sets of biomarkers candidates were combined by the link-test statistics, indicating that 13 genes were potential biomarkers for early diagnosis of osteosarcoma. Among these genes, cytochrome c1 (CYC-1) was selected for further experimental validation. Conclusion: Link-test on datasets from both SELDI-TOF-MS and microarray high-throughput analysis can accelerate the identification of tumor biomarkers. The result confirmed that CYC-1 may be a promising biomarker for early diagnosis of osteosarcoma.
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