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Wang S, Cheng M, Peng P, Lou Y, Zhang A, Liu P. Iron Released after Cryo-Thermal Therapy Induced M1 Macrophage Polarization, Promoting the Differentiation of CD4 + T Cells into CTLs. Int J Mol Sci 2021; 22:ijms22137010. [PMID: 34209797 PMCID: PMC8268875 DOI: 10.3390/ijms22137010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/18/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
Macrophages play critical roles in both innate and adaptive immunity and are known for their high plasticity in response to various external signals. Macrophages are involved in regulating systematic iron homeostasis and they sequester iron by phagocytotic activity, which triggers M1 macrophage polarization and typically exerts antitumor effects. We previously developed a novel cryo-thermal therapy that can induce the mass release of tumor antigens and damage-associated molecular patterns (DAMPs), promoting M1 macrophage polarization. However, that study did not examine whether iron released after cryo-thermal therapy induced M1 macrophage polarization; this question still needed to be addressed. We hypothesized that cryo-thermal therapy would cause the release of a large quantity of iron to augment M1 macrophage polarization due to the disruption of tumor cells and blood vessels, which would further enhance antitumor immunity. In this study, we investigated iron released in primary tumors, the level of iron in splenic macrophages after cryo-thermal therapy and the effect of iron on macrophage polarization and CD4+ T cell differentiation in metastatic 4T1 murine mammary carcinoma. We found that a large amount of iron was released after cryo-thermal therapy and could be taken up by splenic macrophages, which further promoted M1 macrophage polarization by inhibiting ERK phosphorylation. Moreover, iron promoted DC maturation, which was possibly mediated by iron-induced M1 macrophages. In addition, iron-induced M1 macrophages and mature DCs promoted the differentiation of CD4+ T cells into the CD4 cytolytic T lymphocytes (CTL) subset and inhibited differentiation into Th2 and Th17 cells. This study explains the role of iron in cryo-thermal therapy-induced antitumor immunity from a new perspective.
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Affiliation(s)
- Shicheng Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (S.W.); (M.C.); (P.P.); (Y.L.); (A.Z.)
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Man Cheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (S.W.); (M.C.); (P.P.); (Y.L.); (A.Z.)
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Peng Peng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (S.W.); (M.C.); (P.P.); (Y.L.); (A.Z.)
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yue Lou
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (S.W.); (M.C.); (P.P.); (Y.L.); (A.Z.)
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Aili Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (S.W.); (M.C.); (P.P.); (Y.L.); (A.Z.)
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Ping Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (S.W.); (M.C.); (P.P.); (Y.L.); (A.Z.)
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China
- Correspondence: ; Tel.: +86-021-62933231
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Hao D, Ding S, Qiu L, Lv Y, Fei B, Zhu Y, Qin B. Sequential vessel segmentation via deep channel attention network. Neural Netw 2020; 128:172-187. [PMID: 32447262 DOI: 10.1016/j.neunet.2020.05.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 04/22/2020] [Accepted: 05/04/2020] [Indexed: 02/01/2023]
Abstract
Accurately segmenting contrast-filled vessels from X-ray coronary angiography (XCA) image sequence is an essential step for the diagnosis and therapy of coronary artery disease. However, developing automatic vessel segmentation is particularly challenging due to the overlapping structures, low contrast and the presence of complex and dynamic background artifacts in XCA images. This paper develops a novel encoder-decoder deep network architecture which exploits the several contextual frames of 2D+t sequential images in a sliding window centered at current frame to segment 2D vessel masks from the current frame. The architecture is equipped with temporal-spatial feature extraction in encoder stage, feature fusion in skip connection layers and channel attention mechanism in decoder stage. In the encoder stage, a series of 3D convolutional layers are employed to hierarchically extract temporal-spatial features. Skip connection layers subsequently fuse the temporal-spatial feature maps and deliver them to the corresponding decoder stages. To efficiently discriminate vessel features from the complex and noisy backgrounds in the XCA images, the decoder stage effectively utilizes channel attention blocks to refine the intermediate feature maps from skip connection layers for subsequently decoding the refined features in 2D ways to produce the segmented vessel masks. Furthermore, Dice loss function is implemented to train the proposed deep network in order to tackle the class imbalance problem in the XCA data due to the wide distribution of complex background artifacts. Extensive experiments by comparing our method with other state-of-the-art algorithms demonstrate the proposed method's superior performance over other methods in terms of the quantitative metrics and visual validation. To facilitate the reproductive research in XCA community, we publicly release our dataset and source codes at https://github.com/Binjie-Qin/SVS-net.
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Affiliation(s)
- Dongdong Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Song Ding
- Department of Cardiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Linwei Qiu
- School of Astronautics, Beihang University, Beijing 100191, China
| | - Yisong Lv
- School of Continuing Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Baowei Fei
- Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Yueqi Zhu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University, 600 Yi Shan Road, Shanghai 200233, China
| | - Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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Mei K, Hu B, Fei B, Qin B. Phase asymmetry ultrasound despeckling with fractional anisotropic diffusion and total variation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 29:10.1109/TIP.2019.2953361. [PMID: 31751240 PMCID: PMC7370834 DOI: 10.1109/tip.2019.2953361] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
We propose an ultrasound speckle filtering method for not only preserving various edge features but also filtering tissue-dependent complex speckle noises in ultrasound images. The key idea is to detect these various edges using a phase congruence-based edge significance measure called phase asymmetry (PAS), which is invariant to the intensity amplitude of edges and takes 0 in non-edge smooth regions and 1 at the idea step edge, while also taking intermediate values at slowly varying ramp edges. By leveraging the PAS metric in designing weighting coefficients to maintain a balance between fractional-order anisotropic diffusion and total variation (TV) filters in TV cost function, we propose a new fractional TV framework to not only achieve the best despeckling performance with ramp edge preservation but also reduce the staircase effect produced by integral-order filters. Then, we exploit the PAS metric in designing a new fractional-order diffusion coefficient to properly preserve low-contrast edges in diffusion filtering. Finally, different from fixed fractional-order diffusion filters, an adaptive fractional order is introduced based on the PAS metric to enhance various weak edges in the spatially transitional areas between objects. The proposed fractional TV model is minimized using the gradient descent method to obtain the final denoised image. The experimental results and real application of ultrasound breast image segmentation show that the proposed method outperforms other state-of-the-art ultrasound despeckling filters for both speckle reduction and feature preservation in terms of visual evaluation and quantitative indices. The best scores on feature similarity indices have achieved 0.867, 0.844 and 0.834 under three different levels of noise, while the best breast ultrasound segmentation accuracy in terms of the mean and median dice similarity coefficient are 96.25% and 96.15%, respectively.
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Affiliation(s)
- Kunqiang Mei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Bin Hu
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Institute of Ultrasound in Medicine, Shanghai 200233, China
| | - Baowei Fei
- Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75080 USA
| | - Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Zhao W, Liu Q, Lv Y, Qin B. Texture Variation Adaptive Image Denoising With Nonlocal PCA. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 28:5537-5551. [PMID: 31135359 DOI: 10.1109/tip.2019.2916976] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Image textures, as a kind of local variations, provide important information for the human visual system. Many image textures, especially the small-scale or stochastic textures, are rich in high-frequency variations, and are difficult to be preserved. Current state-of-the-art denoising algorithms typically adopt a nonlocal approach consisting of image patch grouping and group-wise denoising filtering. To achieve a better image denoising while preserving the variations in texture, we first adaptively group high correlated image patches with the same kinds of texture elements (texels) via an adaptive clustering method. This adaptive clustering method is applied in an over-clustering-and-iterative-merging approach, where its noise robustness is improved with a custom merging threshold relating to the noise level and cluster size. For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures. Experiments on natural images show the superiority of the proposed transform-domain variation adaptive filtering to traditional PCA-based hard or soft threshold filtering. As a whole, the proposed denoising method achieves a favorable texture-preserving performance both quantitatively and visually, especially for irregular textures, which is further verified in camera raw image denoising.
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Qin B, Jin M, Hao D, Lv Y, Liu Q, Zhu Y, Ding S, Zhao J, Fei B. Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms. PATTERN RECOGNITION 2019; 87:38-54. [PMID: 31447490 PMCID: PMC6708416 DOI: 10.1016/j.patcog.2018.09.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper proposes an effective method for accurately recovering vessel structures and intensity information from the X-ray coronary angiography (XCA) images of moving organs or tissues. Specifically, a global logarithm transformation of XCA images is implemented to fit the X-ray attenuation sum model of vessel/background layers into a low-rank, sparse decomposition model for vessel/background separation. The contrast-filled vessel structures are extracted by distinguishing the vessels from the low-rank backgrounds by using a robust principal component analysis and by constructing a vessel mask via Radon-like feature filtering plus spatially adaptive thresholding. Subsequently, the low-rankness and inter-frame spatio-temporal connectivity in the complex and noisy backgrounds are used to recover the vessel-masked background regions using tensor completion of all other background regions, while the twist tensor nuclear norm is minimized to complete the background layers. Finally, the method is able to accurately extract vessels' intensities from the noisy XCA data by subtracting the completed background layers from the overall XCA images. We evaluated the vessel visibility of resulting images on real X-ray angiography data and evaluated the accuracy of vessel intensity recovery on synthetic data. Experiment results show the superiority of the proposed method over the state-of-the-art methods.
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Affiliation(s)
- Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Mingxin Jin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dongdong Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yisong Lv
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - Yueqi Zhu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Jiao Tong University, 600 Yi Shan Road, Shanghai 200233, China
| | - Song Ding
- Department of Cardiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Baowei Fei
- Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA
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Jin M, Hao D, Ding S, Qin B. Low-rank and sparse decomposition with spatially adaptive filtering for sequential segmentation of 2D+t vessels. ACTA ACUST UNITED AC 2018; 63:17LT01. [DOI: 10.1088/1361-6560/aad8e0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Mingxin Jin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
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Yu C, Sun J. Signal separation from X-ray image sequence using singular value decomposition. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.01.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Micro-Droplet Detection Method for Measuring the Concentration of Alkaline Phosphatase-Labeled Nanoparticles in Fluorescence Microscopy. SENSORS 2017; 17:s17112685. [PMID: 29160812 PMCID: PMC5712791 DOI: 10.3390/s17112685] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 11/17/2017] [Accepted: 11/19/2017] [Indexed: 12/11/2022]
Abstract
This paper developed and evaluated a quantitative image analysis method to measure the concentration of the nanoparticles on which alkaline phosphatase (AP) was immobilized. These AP-labeled nanoparticles are widely used as signal markers for tagging biomolecules at nanometer and sub-nanometer scales. The AP-labeled nanoparticle concentration measurement can then be directly used to quantitatively analyze the biomolecular concentration. Micro-droplets are mono-dispersed micro-reactors that can be used to encapsulate and detect AP-labeled nanoparticles. Micro-droplets include both empty micro-droplets and fluorescent micro-droplets, while fluorescent micro-droplets are generated from the fluorescence reaction between the APs adhering to a single nanoparticle and corresponding fluorogenic substrates within droplets. By detecting micro-droplets and calculating the proportion of fluorescent micro-droplets to the overall micro-droplets, we can calculate the AP-labeled nanoparticle concentration. The proposed micro-droplet detection method includes the following steps: (1) Gaussian filtering to remove the noise of overall fluorescent targets, (2) a contrast-limited, adaptive histogram equalization processing to enhance the contrast of weakly luminescent micro-droplets, (3) an red maximizing inter-class variance thresholding method (OTSU) to segment the enhanced image for getting the binary map of the overall micro-droplets, (4) a circular Hough transform (CHT) method to detect overall micro-droplets and (5) an intensity-mean-based thresholding segmentation method to extract the fluorescent micro-droplets. The experimental results of fluorescent micro-droplet images show that the average accuracy of our micro-droplet detection method is 0.9586; the average true positive rate is 0.9502; and the average false positive rate is 0.0073. The detection method can be successfully applied to measure AP-labeled nanoparticle concentration in fluorescence microscopy.
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