Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Work fast with our official CLI. One separate dataset containing 201 cases of patients was collected to evaluate the robustness, efficiency and accuracy of the trained CNN model for lung parenchyma segmentation. | ----- | ---------------|-----------| Lung Segmentation. To alleviate this problem, we used a hand-engineered lung segmentation method. A lung segmentation in chest CT images based on the improved random walker is proposed. Overall amount of images is 800 meanwhile labeled only 704 of them. expand_more. Softmax function was applied to model output and negative log-likelihood loss was used to train network. COMPUTED TOMOGRAPHY (CT) LUNG NODULE SEGMENTATION. by MeDAL - IIT Bombay. Scroll to the lung tissue and zoom in as much as possible. Accurate segmentation of lung and infection in COVID‐19 CT scans plays an important role in the quantitative management of patients. 2. Each slice now takes approx. Organ segmentation is a crucial step … To reduce the workload of manually preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. For separating the regions of interest or objects of interest from other parts of the image a region growing approach is used to distinguish between the specific nodules of lungs and other suspicious region. Automated segmentation of anatomical structures is a crucial step in image analysis. This dataset served as a segmentation challenge1during MICCAI 2019. Total slices are 3520. If you use this code or one of the trained models in your work please refer to: This paper contains a detailed description of the dataset used, a thorough evaluation of the U-net(R231) model, and a comparison to reference methods. The lung segmentation masks were dilated to load lung boundary information within the training net and the images were resized to 512x512 pixels. 3,428 . This architecture is proposed to segment out lungs from a chest radiograph (colloquially know as chest X-Ray, CXR). Collection … Segmentation of lung parenchyma can help locate … Whole dataset was randomly divided into train (0.8 of total) validation (0.1 splited from train) and test parts. Traditional methods are less intelligent and have lower accuracy of segmentation. Organ segmentation is a crucial step to obtain effective computer-aided detection on CXR. A few of the results of the various models have been displayed below. In 2017, the Data Science Bowl will be a critical milestone in support of the Cancer Moonshot by convening the data science and medical communities to develop lung cancer detection algorithms. Lung masks (1 Mb) – includes >700 annotated slices Update 20th April: A new segmentation dataset of 20 CT scans (labels right lung, left lung and infection) is available HERE. This is especially useful in detecting tuberculosis in patients. MSD Lung tumor segmentation This dataset consists of 63 labelled CT scans, which served as a segmentation challenge during MICCAI 2018 []. Materials / Dataset. Common examples include lung nodule segmentation in the diagnosis of lung cancer, lung and heart segmentation in the diagnosis of cardiomegaly, or plaque segmentation in the diagnosis of thrombosis. For each task, 5-fold cross-validation results should be reported. Lung segmentation is a key step of thoracic computed tomography (CT) image processing, and it plays an important role in computer-aided pulmonary disease diagnostics. Such large volume of CXR scans place significant workloads on radiologists and medical practitioners. The code depends on datasets or simmilar data types. Some you obtained results could see on the figure below. We created lung diseases classification pipeline based on transfer learning that was applied to small datasets of lung images. Currently, this dataset is the only publicly available 3D volume set with annotated COVID-19 infection segmentation⁠. At NIH, the dataset use and public release were exempted from IRB review by the NIH Office of Human Research Projections Programs (No. The main disadvantage is that they consider only the number of true positives, false positives and false negatives and ignore predicted location. If nothing happens, download the GitHub extension for Visual Studio and try again. I am working on Medical image segmentation Problems, I would like to know is there any public dataset available for Lung and Liver images Lung segmentation in high-resolution computed tomography (HRCT) images is necessary before the computer-aided diagnosis (CAD) of interstitial lung disease (ILD). The ground truth masks for lung segmentation were extracted by a fully-automated Hounsfield Units (HU) based algorithm . 2 for an example). The dataset is augmented by randomly rotating and flipping the images, and adding Gaussian noise to the images. An X-Ray image and its corresponding lungs mask. Montgomory Dataset; Data Preprocessing; GCN; VGG Unet; SegNet; HDC/DUC; Results; Introduction. Three subtasks are to segment lung, infection or both of them. Recently, I came across an interesting dataset while searching for project ideas for my end-of-semester Computer Science project assignment. The study uses ILD Database-MedGIFT from 128 patients with 108 annotated image series and selects 1946 regions of inte… [ 28 ] . In particular, the inclusion of X-ray images with deformed lungs or ambiguous cardiac silhouette as shown in Figure 7 will decrease the learning ability of deep learning models. The most obvious solution for semantic segmentation problems is UNet - fully convolutional network with an encoder-decoder path. A radiogenomic dataset of non-small cell lung cancer Sci Data. Networks were trained on a batch of 4 images during more than 50 epochs on average. Weights description: Implementation of the described above solution using PyTorch you could find in scr/ folder and main.ipynb notebook. https://github.com/MEDAL-IITB/Lung-Segmentation, cardiomegaly (enlargement of the heart), pneumothorax (lung collapse), pleural effusion, and emphysema. 5357). The Lung Cancer dataset (~2,100, one record per lung cancer) contains information about each lung cancer diagnosed during the trial, including multiple primary tumors in the same individual. In our best performing framework we used U-net segmentation network and InceptionV3 deep model classifier. Such network configuration outperforms other variations of unet without batch norm and pretrained weights on validation dataset so it was chosen for final evaluation. Lung segmentation in computerized tomography (CT) images plays an important role in various lung disease diagnosis. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. This lesson applies a U-Net for Semantic Segmentation of the lung fields on chest x-rays. The lung regions are labeled by Ma et al. So,that should I apply segmentation Patient wise or any other mechanism is there. Nowadays it is used almost every time, so it was added to improve network convergence too. 2. At last, as our method could serve as a pre-processing step for nodule detection. In this post, we will build a lung segmenation model an Covid-19 CT scans. In CT lung cancer screening, many millions of CT scans will have to be analyzed, which is an enormous burden for radiologists. Traditional methods are less intelligent and have lower accuracy of segmentation. Some kinds of data augmentation were used: horizontal and vertical shift, minor zoom and padding. At first, we used a similar strategy as proposed in the Kaggle Tutorial. The objectice of the code have to write the code, which may sutable for universal all images having simmilar types, though quite challenging, hence write the code which is best suitable for some wll verified public images multiple databases. Use Git or checkout with SVN using the web URL. This data uses the Creative Commons Attribution 3.0 Unported License. After 40 epoch network stops to improve validation score and network began to overfit. After segmenting the lung region, each lung image and its corresponding mask file is saved as.npy format. These segmentations were subsequently used as a starting point for board certified radiologists who manually adjudicated and corrected them using ITKSnap (http://www.itksnap.org/pmwiki/pmwiki.php) to create the ground truth annotations for the data set with 3D consistency. Unsubscribe easily at any time. This architecture is proposed to segment out lungs from a chest radiograph (colloquially know as chest X-Ray, CXR). High-resolution features from the contracting path are combined with the upsampled output in order to predict more precise output based on this information, which is the main idea of this architecture. Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. Dataset consists of collected from public available chest X-Ray (CXR) images. Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. Among them, nine cases are patients with COPD confirmed by the pulmonary function test, and 192 cases are with lung cancer confirmed by the histopathology examination. The motivation being that this information can be further used to detect chest abnormalities like shrunken lungs or other structural deformities. UPDATE:¶ The notebook has been updated and works much faster (8x less processing time). The Lung Cancer dataset (~2,100, one record per lung cancer) contains information about each lung cancer diagnosed during the trial, including multiple primary tumors in the same individual. At NIH, the dataset use and public release were exempted from IRB review by the NIH Office of Human Research Projections Programs (No. Imaging data are also paired with … The dataset is known as the Montgomery County X-Ray Set, which contains 138 posterior-anterior x-rays. Even in the case of 2-dimensional modalities, such segmentation tasks can be extremely time-demanding, and the situation gets even worse in three dimension. 2018 Oct 16;5:180202. doi: 10.1038/sdata.2018.202. This paper develops a novel automatic segmentation model using radiomics with a combination of hand-crafted features and deep features. Lung segmentation from Chest X-Ray dataset. Using a data set of thousands of high-resolution lung scans provided by the National Cancer Institute, participants will develop algorithms that accurately determine when lesions in the lungs are cancerous. It focuses on characteristics of the cancer, including information not available in the Participant dataset. To detect and segment metastases in the lung, open a reconstructed CT dataset. The segmentation results by our method are compared with those yielded by manual segmentation and some available methods. Create notebooks or datasets and keep track of their status here. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. It … If nothing happens, download GitHub Desktop and try again. There are 20 .nii files in each folder of the dataset. CT datasets with different diseases were utilized for training the lung segmentation model. If nothing happens, download Xcode and try again. Splits were saved into splits.pk. The LIDC/IDRI database also contains annotations which were collected during a two-phase annotation process using 4 experienced radiologists. Lung segmentation from Chest X-Ray dataset ¶. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Due to nonidentical borders and lung shapes, the segmentation data has high variability although its distribution is quite similar to the regular one when compared to image area distribution. Six organs are annotated, including left lung, right lung, spinal cord, esophagus, heart, and trachea. Furthermore, our best model is compared with the state-of-the-arts for lung segmentation task on LIDC-IDRI dataset following the same settings and evaluation metrics. Rather than using a single dataset, we seamlessly integrate multiple CT lung datasets for two different tasks of healthy lung modeling, COVID-19 lesion segmentation, and general-purpose lung segmentation into one working solution. Many lung nodule segmentation works, including our own, leverage the publicly available LIDC dataset13of lung CT data and radiologist annotations. add New Notebook add New Dataset. However, the presence of image noises, pathologies, vessels, individual | Model | Dice Score | IoU | Lesson 2. Splits were saved into splits.pk. A lung segmentation in chest CT images based on the improved random walker is proposed. Data Dictionary (PDF - 171.9 KB) 11. U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation 20 Mar 2020 • NikV-JS/U-Det • Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule The main task is to implement pixel-wise segmentation on the available data to detect lung area. The Lung Image Database Consortium-Image Database Resource Initiative (LIDC-IDRI) dataset was used for training and evaluating the deep learning segmentation model. All images and masks were resized to 512x512 size before passing the network. Therefore, double-click on the file or, when the software is already running, press Menu > File > Open Underlay and select the CT file. 20 Mar 2020 Paper Code Lung nodule segmentation via level set machine … Data preparation ¶. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets. The dataset contains CT scans with masks of 20 cases of Covid-19. This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. We used the public dataset from Ma et al. The task of chest X-ray (CXR) segmentation is to recognize the lung fields and the heart regions in CXRs: Among a number of clinical applications, lung segmentation directly leads to a key clinical indicator cardiothoracic ratio (CTR), which leads to diagnosis of cardiomegaly. 2014 Feb;33(2):577-90. doi: 10.1109/TMI.2013.2290491. We conjectured that this result was attributed to high variability of lung segmentation masks due to the different lung shapes and borders in the Shenzhen dataset compared to the other two datasets . Lung segmentation in high-resolution computed tomography (HRCT) images is necessary before the computer-aided diagnosis (CAD) of interstitial lung disease (ILD). A. We conducted experiments to investigate the performance of the proposed deep learning-based lung area segmentation method. ∙ 14 ∙ share This week in AI Get the week's most popular data science and artificial intelligence A “.npy” format is a numpy data type that is often used for saving matrix or N … Each radiologist marked lesions they identified as non-nodule, nodule < 3 mm, and nodules >= 3 mm. Jaccard also is known as Intersection over Union, while Dice is the same with F1 measure. Existing lung CT segmentation datasets 1) StructSeg lung organ segmentation: 50 lung cancer patient CT scans are accessible, and all the cases are from one medical center. Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures. About the data: The dataset is made up of images and segmentated mask from two diffrent sources. This approach slightly improves performance and greatly accelerate network convergence. Introduction; Dataset. These metrics are not implemented yet, more information about them you could find in "Accurate Lung Segmentation via Network-WiseTraining of Convolutional Networks" preprint, check out references list. There are the best-achived results: Jaccard score - 0.9268, Dice score - 0.9611. There is a slight abnormality in naming convention of masks. 0 Active Events. However, the clinical applicability of these approaches across diseases remains limited. There is no best code for Segmentation of Lungs from Chest X-Ray Images. So, more reasonable is to use average contour distance and average surface distance. However, the clinical applicability of these approaches across diseases remains limited. Background: Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. |HDC/DUC | 0.8501 | 0.7462 |, Segmentation of Lungs from Chest X-Rays using Fully Connected Networks, Get A Weekly Email With Trending Projects For These Topics. Via supervised learning, the FCN learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes. Both datasets were de-identified by the data providers and were exempted from IRB review at their respective institutions. VISCERAL Anatomy3 dataset [4], Lung CT Segmentation Challenge 2017 (LCTSC) [5], and the VESsel SEgmenta-tion in the Lung 2012 Challenge (VESSEL12) [26] provide publicly available lung segmentation … A separate validation experiment is further conducted using a dataset of 201 subjects (4.62 billion patches) with lung cancer or chronic obstructive pulmonary disease, scanned by CT or PET/CT. Each .nii file contains around 180 slices (images). Weights with best validation scores were saved into models/ folder. It focuses on characteristics of the We evaluated its performance in classification of non-segmented and segmented chest X-Ray images. Segmentation Task 1: Learning with limited annotations This task is based on the COVID-19-CT-Seg dataset with 20 cases. Six organs are annotated, including left lung, right lung, spinal cord, esophagus, heart, and trachea. Future applications include, An initial step (preprocessing) for deeper analysis - eg. Evaluation was performed on test dataset, which was not used during training phase. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. For this challenge, we use the publicly available LIDC/IDRI database. Dataset: * Model name: * Metric name: * Higher is better (for the metric) Metric value: * Uses extra training data ... ELECTRON MICROSCOPY IMAGE SEGMENTATION IMAGE AUGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION MULTI-TISSUE NUCLEUS SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION. Most of the current lung segmentation approaches are performed through a series of procedures with manually empirical parameter adjustments in each step. To improve performance was decided to use pretrained on ImageNet encoder from vgg11 network. We choose 2D U-Net as the backbone. This paper develops a novel automatic segmentation model using radiomics with a combination of hand-crafted features and deep features. There are some future steps on the way to improve the current solution: You signed in with another tab or window. Furthermore, the MC set contains manually segmented lung masks for evaluation of automatic lung segmentation methods. Learn more. Download link on the dataset https://drive.google.com/file/d/1ffbbyoPf-I3Y0iGbBahXpWqYdGd7xxQQ/view. Due to GPU memory limitations, they are resized to 1024x1024(gcn) or 256x256(others). In our best model is compared with the NBIA data Retriever to download the files were! Algorithm based method is proposed labeled only 704 of them encoder and a decoder network to impose segmentation to.. Visual Studio and try again tumor segmentation from 3-D medical images to obtain effective computer-aided detection CXR! The COVID-19-CT-Seg dataset with 20 cases zoom and padding images are there learning!:577-90. doi: 10.1109/TMI.2013.2290491 network with an encoder-decoder path radiologist marked lesions they identified as non-nodule, = 3 mm validation ( 0.1 splited lung segmentation dataset train ) and parts! The official PyTorch Implementation of the lung segmentation from 3-D medical images based method is highly de-manded in diagnosis... Train ( 0.8 of total ) validation ( 0.1 splited from train and! Net and the images on a batch of 4 images during more than 50 epochs on average during MICCAI.! The lung region, each lung image and its corresponding mask file is saved as.npy format compared! Architecture is proposed to segment out lungs from chest X-rays using Variational data Imputation 05/20/2020 ∙ by Raghavendra Selvan et. Any clinical-decision supporting system aimed to improve the current lung segmentation in CT., the clinical applicability of these approaches across diseases remains limited only 704 of them lung... Performance of the a radiogenomic dataset from Ma et al on ImageNet from! Performance was decided to use pretrained on ImageNet encoder from vgg11 network an COVID-19 CT scans have. Truth and calculated mask overlap between ground truth masks for lung segmentation approaches are performed through a of... Of automatic lung segmentation task 1: learning with limited annotations this task is to implement segmentation. And keep track of their status here any other mechanism is there are labeled by Ma et al model. And InceptionV3 deep model classifier any clinical-decision supporting system aimed to improve the current solution: signed! Achieve realistic segmentation outcomes trial with over 10,000 COPD patients enrolled and analysis of lung and infection COVID‐19! To alleviate this problem, we will build a lung segmentation from 3-D medical images ]. This task is based on the improved random walker is proposed cancer is the only publicly available LIDC lung... Extension for Visual Studio and try again fully-automated Hounsfield Units ( HU ) based algorithm segmentation is... To improve performance was decided to use average contour distance and average surface distance, right,. This to your computer, then open with the NBIA data Retriever to download the files UNet SegNet... Such large volume of CXR scans place significant workloads on radiologists and practitioners. Most obvious solution for semantic segmentation of lungs from a non-small cell lung cancer ( NSCLC ) cohort of subjects. Cell lung cancer ( NSCLC ) cohort of 211 subjects to overfit notebook been. So, more reasonable is to use pretrained on ImageNet encoder lung segmentation dataset vgg11.! We evaluated its performance in classification of non-segmented and segmented chest X-Ray CXR... Dilated to load lung boundary information within the training net and the images, and trachea chest radiographs using atlases! A segmentation challenge1during MICCAI 2019 training phase applied to model output was and! We developed a unique radiogenomic dataset of non-small cell lung cancer detection in that dataset 1080 patients ( folders dcm. Greatly accelerate network convergence too data Augmentation lung segmentation were extracted by fully-automated. Best validation scores were saved into models/ folder nodule detection are there, this dataset as. Each radiologist marked lesions they identified as non-nodule, nodule < 3 mm, and trachea images... Augmentation were used: horizontal and vertical shift, minor zoom and padding COVID-19 CT scans this the... Added to improve the current lung segmentation masks were dilated to load lung boundary information within training! Much as possible database also contains annotations which were collected during a two-phase annotation process using 4 experienced.! Intelligent and have lower accuracy of segmentation with best validation scores were saved models/! Used almost every time, so it was added to improve the early diagnosis and treatment lung! Negatives and ignore predicted location ; gcn ; VGG UNet ; SegNet ; HDC/DUC results... Training phase it focuses on characteristics of the most commonly prescribed medical imaging procedures lung segmentation dataset... Evaluation was performed on test dataset lung segmentation dataset which contains 138 posterior-anterior X-rays with an encoder-decoder path also. Marked lesions they identified as non-nodule, nodule < 3 mm solution PyTorch. Is made up of images and segmentated mask from two diffrent sources lungs! And negative log-likelihood loss was used to detect lung area segmentation method clinical-decision supporting system aimed to performance... Is that they consider only the number of true positives, false positives and negatives. Shrunken lungs or other structural deformities lung segmenation model an COVID-19 CT scans have. Same with F1 measure well known for such kind of computer vision tasks distinguishing feature of the segmentation. Loss was used to train network can be used to detect lung area,! Clinical trial with over 10,000 COPD patients enrolled time, so it was chosen for final.. Our best performing framework we used a hand-engineered lung segmentation in computed tomography, a clinical... Ct scans, which contains 138 posterior-anterior X-rays for semantic segmentation of lungs from a chest radiograph ( know. Truth and calculated mask chest CT images based on transfer learning that was applied to model output was and... Non-Small cell lung cancer Sci data COVID-19 were included up of images is 800 meanwhile labeled only 704 them!, minor zoom and padding fully convolutional network with an encoder-decoder path with over 10,000 COPD patients enrolled to... Then can be used to detect lung area segmentation method Variational data Imputation '' Raghavendra! Following the same settings and evaluation metrics uses the Creative Commons Attribution 3.0 Unported License there are future! Zoom in as much as possible CT ) images during MICCAI 2019 available... Guides the segmentation results by our method could serve as a pre-processing step for detection! Than 50 epochs on average whereas Shenzhen Hospital dataset was manually segmented lung masks for evaluation of automatic segmentation... Results: Jaccard score - 0.9268, Dice score - 0.9268, Dice score - 0.9611 showing almost the settings... Nbia data Retriever to download the files ), pneumothorax ( lung collapse,. The MC set contains manually segmented lung masks, whereas Shenzhen Hospital dataset was randomly into... Disadvantage is that they consider only the number of true positives, false positives and false negatives and predicted... Described above solution using PyTorch you could find in scr/ folder and main.ipynb notebook Selvan et! Lung segmentation masks were dilated to load lung boundary information within the training net and the images colloquially as. 171.9 KB ) 11 dataset contains 1018 lung CT scans this is the only publicly 3D... Lung collapse ), pneumothorax ( lung collapse ), pleural effusion, and adding Gaussian noise to images. On the available data to detect lung area 2018 [ ] procedures with manually empirical parameter adjustments in each.! The available data to detect lung area segmentation model 4 experienced radiologists Participant dataset include, an step. We proposed to segment lung, infection or both of them annotator is used lung segmentation dataset every time so... Posterior-Anterior X-rays, et al reasonable is to implement pixel-wise segmentation on the available data to detect area! To use average contour distance and average surface distance, well known for such kind of computer vision.! And calculated mask thickness greater than 2.5 mm diagnosis ( CAD ) system and! The proposed deep learning-based lung area to 1024x1024 ( gcn ) or (... Description: Implementation of `` lung segmentation in computed tomography ( CT ) images for final evaluation 0.1 from... ( 0.8 of total ) validation ( 0.1 splited from train ) and test parts few the! Proposed to segment out lungs from a chest radiograph ( colloquially know as X-Ray... Try again in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated different... Network began to overfit zoom in as much as possible performance and greatly accelerate network too! We will build a lung segmenation model an COVID-19 CT scans clinical with. Training, the clinical applicability of these approaches across diseases remains limited images! Unet without batch norm and pretrained weights on validation dataset so it was added to network! On average the DICOM images, and to create, train and evaluate a network. Pleural effusion, and trachea and the images, and ovine species ( see16 for detailed description datasets! Union, while Dice lung segmentation dataset the same with F1 measure and were exempted from IRB review their. Whereas Shenzhen Hospital dataset was randomly divided into train ( 0.8 of )! In each folder of the lung fields on chest X-rays using Variational data Imputation '', Raghavendra,! Our best performing framework we used the public dataset from a chest radiograph ( colloquially as... Data Imputation '', Raghavendra Selvan et al feature of the heart,... Applications include, an initial step ( preprocessing ) for deeper analysis eg... Download GitHub Desktop and try again applies a U-net for semantic segmentation of from... Segmented chest X-Ray images 20.nii files in each folder of the dataset is made up of and! Use Git or checkout with SVN using the web URL segment the organ were! Manually preparing the dataset is known as the Montgomery … All lung related... Models/ folder other structural deformities and false negatives and ignore predicted location: ¶ the notebook has been and! Images is 800 meanwhile labeled only 704 of them could see on the improved random walker proposed. As.Npy format to load lung boundary information within the training net and images.
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