I had a hard time going through other people’s Github and codes that were online. The remainder of this paper is structured as follows. Relevant publications Hanxiao Zhang, Yun Gu, Yulei Qin, Feng Yao, Guang-Zhong Yang, Learning with Sure Data for … Standardized representation of the LIDC annotations using DICOM. But it is enough to get a model running as one can see from the provided examples. 11/24/2019 ∙ by Jiancheng Yang, et al. For a limited set of cases, LIDC sites were able to identify diagnostic data associated with the case. Facts. Badges are live and will be dynamically updated with the latest ranking of this paper. TCIA is a service which de-identifies and hosts a large archive of medical images of cancer accessible for public download. 2. 1. Images are processed using local feature descriptors and transformation methods before input into classifiers. The purpose of the database is to provide a web-accessible resource of a format suitable to aid and test the development of CAD of pulmonary nodules. 0000005607 00000 n The header data is contained in .mhd files and multidimensional image data is stored in .raw files. Better quality. I used SimpleITKlibrary to read the .mhd files. 3, we describe the LIDC dataset and our experimental setup. For the LIDC-IDRI, 4 radiologist scored nodules on a scale from 1 to 5 for different properties. My concern with LIDC is that it might encourage overfitting to that dataset. Facebook API. The 7th place team, for example, probably would have placed top 5 if they had seen that LIDC had malignancy. configure pylidc to know where the scans are located, follow these steps. [20] MS 78.70% – 47 Han et al. 466 0 obj <> endobj Helps developers build, grow and monetize their business. We obtained state-of-the art performance for detection and malignancy regression on the LIDC-IDRI database. Issues. In Sec. For the three-class scan-level classification we obtained an accuracy of 78%. There has been considerable debate over 2D and 3D representation learning on 3D medical images. [19] NA 54.32% – 914 Chen et al. Diagnosis Data. 0000036990 00000 n hތRmHSQ~�����;5���6El�e#h�Z�iΖD��q��-��8���2F��I�Y3I1¢+�I�7ZbA&V8�>(��ѹ�P�?�p�. SOTA for Lung Nodule Classification on LIDC-IDRI (Acc metric) SOTA for Lung Nodule Classification on LIDC-IDRI (Acc metric) Browse State-of-the-Art Methods Trends About RC2020 Log In/Register; Get the weekly digest × Get the latest machine learning methods with code. The discussions on the Kaggle discussion board mainly focussed on the LUNA dataset but it was only when we trained a model to predict the malignancy of the individual nodules/patches that we were able to get close to the top scores on the LB. 0000000016 00000 n 3D Neural Architecture Search (NAS) for Pulmonary Nodules Classification. 13, pp. The classification results of state-of-the-art methods are listed in Table 4. The LIDC/IDRI data set is publicly available, including the annotations of nodules by four radiologists. The remainder of this paper is structured as follows. Social. I hope that my explanation could help those who first start their research or project in Lung Cancer detection. Presented during the January 7, 2019 NCI Imaging Community Call Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. 2, we discuss the related work. More than 50 million people use GitHub to discover, fork, and contribute to over 100 million projects. The CNN is best CT image classification. View the Project on GitHub xunweiyee/lung-cancer-image-classification. Lung cancer image classification in Python using LIDC dataset. A curve on the image evolves according to some PDE. Some of the codes are sourced from below. MusixMatch. ... Read More Social. The scripts uses some standard python libraries (glob, os, subprocess, numpy, and xml), the python library SimpleITK.Additionally, some command line tools from MITK are used. See this publicatio… For a limited set of cases, LIDC sites were able to identify diagnostic data associated with the case. This data uses the Creative Commons Attribution 3.0 Unported License. Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. 4.2.5. ... here is the link of github where I learned a lot from. 0000036260 00000 n Solid State Nodule Classification Dataset ... (484 solid nodules selected from LIDC-IDRI dataset) served for malignancy prediction are objectively revealed. We use the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), where both lung nodule CT and nodule annotations are provided by radiologists. High-level feature. The LIDC dataset 19 is a publicly available set of 1018 lung CT scans collected through various universities and organizations. In Sec. In addition to the CT image data, manual annotations by anonymous radiologists for each scan are provided. Description With the TrueLayer API, we cannot request transactions specifying a date in the future because the request fails. They can be either obtained by building MITK and enablingthe classification module or by installing MITK Phenotypingwhich contains allnecessary command line tools. The 7th place team, for example, probably would have placed top 5 if they had seen that LIDC had malignancy. 0 Reinventing 2D Convolutions for 3D Medical Images. Q&a. <]/Prev 1234230>> Tartar A, Akan A and Kilic N: A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers. %PDF-1.3 %���� Experiments show that the Med3D can accelerate the training convergence speed of target 3D medical tasks 2 times compared with model pre-trained on Kinetics dataset, and 10 times compared with training from scratch as well … Arthur Vichot, né le 26 novembre 1988 à Colombier-Fontaine (), est un coureur cycliste français professionnel de 2010 à 2020.. Passé professionnel en 2010 au sein de l'équipe La Française des jeux, Arthur Vichot a un profil de puncheur à l'aise sur des courses vallonnées. The Data Science Bowl is an annual data science competition hosted by Kaggle. 2014.PubMed/NCBI. Now he is working at the School of Computer Science and Technology, Hangzhou … 0000035538 00000 n 466 28 lidc-binary-classification/README.md at master ... - GitHub xref 2014:4651–4654. 0000059102 00000 n SOTA for Lung Nodule Segmentation on LIDC-IDRI (IoU metric) SOTA for Lung Nodule Segmentation on LIDC-IDRI (IoU metric) Browse State-of-the-Art Methods Reproducibility . Extensive experimental results demonstrate the effectiveness of our method on classifying malignant and benign nodules. These annotations are made with respect to the following types of structures: 1. 3, we describe the LIDC dataset and our experimental setup. We obtained state-of-the art performance for detection and malignancy regression on the LIDC-IDRI database. 0000036812 00000 n In total, 888 CT scans are included. The original DICOM files for LIDC-IDRI images can be downloaded from the LIDC-IDRI website. Typically in a sliding window fashion ($\leadsto$ a lot of redundant computation). degree in electrical information engineering and the Ph.D. degree in intelligent information processing from Xidian University in 2009 and 2015, respectively. ... Read More Facts. 0000162636 00000 n 0000026194 00000 n These annotations are made with respect to the following types of structures: 1. Classification performance on our own dataset was higher for scan- than for nodule-level predictions. Related work Label Accuracy AUC Sample size Zinovev et al. provided in the Lung Image Database Consortium (LIDC) data-set,19 where the degree of nodule malignancy is also indicated by the radiologist annotators. 0000036088 00000 n Solid State Nodule Classification Dataset ... (484 solid nodules selected from LIDC-IDRI dataset) served for malignancy prediction are objectively revealed. Cons : Need a lot of data. I am using convolutional neural network to do classification for lung cancer data set ... etc. Handcraft feature extracting is slow. %%EOF References [1] K. Murphy, B. van Ginneken, A. M. R. Schilham, B. J. de Hoop, H. A. Gietema, and M. Prokop, “A large scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification,” Medical Image Analysis, vol. But one thing it takes time consumption. 493 0 obj <>stream We then present our results in Sec. https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI, use the pylidc library to process image annotations and segmentations (identifying malignant vs benign and the locations of the nodules), resample to 1mm x 1mm x 1mm and process HU values of different scanners, export cropped regions around the nodules in 2 ways: 3D cubes, 2D slices, create a new environment (e.g. Each CT scan has dimensions of 512 x 512 x n, where n is the number of axial scans. Basic idea of PDEs for segmentation. tcia-diagnosis-data-2012-04-20.xls 0000003384 00000 n The images were formatted as .mhd and .raw files. Ability to capture "true" segmentation; Free parameter choices; Stability; Smoothness; Topology; A simple model. There are about 200 images in each CT scan. Lung cancer image classification in Python using LIDC dataset. In Sec. Standardized representation of the LIDC annotations using DICOM AndreyFedorov* 1 ,MatthewHancock 2 ,DavidClunie 3 ,MathiasBrockhausen 4 ,JonathanBona 4 ,JustinKirby 5 , John Freymann 5 , Steve Pieper 6 , Hugo Aerts 1,7 , Ron Kikinis 1,8,9 , Fred Prior 4 1 Brigham and Women’s Hospital, Boston, MA For this challenge, we use the publicly available LIDC/IDRI database. 0000001919 00000 n Diagnosis Data. The LUNA16 challenge is therefore a completely open challenge. But medical data sets aren’t big enogh. 0000005185 00000 n Classification performance on our own dataset was higher for scan- than for nodule-level predictions. This classification was performed both on nodule- and scan-level. Some patients in the LIDC-IDRI dataset have very small nodules or non-nodules. 0000019638 00000 n (acceptance rate 27%) Deep learning. In this paper, we propose a new deep learning method to improve classification accuracy of pulmonary nodules in computed tomography (CT) scans. Zhou M., Shen W., Yang F., and Tian J., “Multi-scale Convolutional Neural Networks for Lung Nodule Classification”, The 24th International Conference on Information Processing in Medical Imaging (IPMI 2015), Isle of Skye, Scotland, 2015. In Sec. Webhooks. Classification performance on our own dataset was higher for scan- than for nodule-level predictions. 0000006367 00000 n The meta_csv data contains all the information and will be used later in the classification stage. There were a total of 551065 annotations. From Oct. 2012 to Sep. 2013, he studied at the University of Technology, Sydney, NSW, Australia, as a visiting Ph.D. student. The way I found the LIDC malignancy information is actually a funny story. 0000004688 00000 n Thus, they do not contain masks. 0000006029 00000 n Fei Gao received the B.Sc. At equilibrium, the curve represents the boundary of segmentation. Metadata. Finally, the classification of lung nodule candidates into nodules and non-nodules is done using a convolutional neural network. Lung cancer is one of the most dangerous cancers. Each image is 28-by-28-by-1 pixels and there are 10 classes. This is the preprocessing step of the LIDC-IDRI dataset - jaeho3690/LIDC-IDRI-Preprocessing. Each radiologist marked lesions they identified as non-nodule, nodule < 3 mm, and nodules >= 3 mm. This classification was performed both on nodule- and scan-level. provided in the Lung Image Database Consortium (LIDC) data-set,19 where the degree of nodule malignancy is also indicated by the radiologist annotators. Our method uses a novel 15-layer 2D deep convolutional neural network architecture for automatic feature extraction and classification of pulmonary candidates as nodule or nonnodule. Lung nodules whose largest diameter is greater than 3mm. You signed in with another tab or window. Github | Follow @sailenav. RC2020 Trends. For classification and regression tasks, you can use trainNetwork to train a convolutional neural network (ConvNet, CNN) for image data, a recurrent neural network (RNN) such as a long short-term memory (LSTM) or a gated recurrent unit (GRU) network for sequence data, or a multi-layer perceptron (MLP) network for numeric feature data. We obtained state-of-the art performance for detection and malignancy regression on the LIDC-IDRI database. In addition to the CT image data, manual annotations by anonymous radiologists for each scan are provided. Q&A for Work. trailer pros : It saves time and money. Let’s you legally display lyrics of over 640k artists and 13M tracks on your app or website ... Read More Lyrics. Of all the annotations provided, 1351 were labeled as nodules, rest were la… 2, we discuss the related work. Define the network architecture. 0000002083 00000 n 0000000856 00000 n Implemented in 2 code libraries. Specify the size of the images in the input layer of the network and the number of classes in the fully connected layer before the classification layer. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Get random Facts on different topics. Browse our catalogue of tasks and access state-of-the-art solutions. ∙ Shanghai Jiao Tong University ∙ 0 ∙ share . We obtained state-of-the art performance for detection and malignancy regression on the LIDC-IDRI database. The nodule classification subnetwork was validated on a public dataset from LIDC-IDRI, on which it achieved better performance than state-of-the-art approaches and surpassed the performance of experienced doctors based on image modality. This example shows how to create and train a simple convolutional neural network for deep learning classification. This repository contains code to pre-process the LIDC-IDRI dataset of CT-scans with pulmonary nodules into a binary classification problem, easy to use for learning deep learning, Download the original scans using the steps from this website: https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI, (note we need scikit-image version 0.13 since replacement of measure.marching_cubes with measure.marching_cubes_lewiner in version 0.14 breaks compatibility with pylidc (as of yet), conda install jupyter numpy pandas feather-format scikit-image=0.13, Currently, the code uses the pylidc function 'cluster_annotations' twice: ones to create a DataFrame of annotations, a second time to export the images. Load the Japanese Vowels data set as described in [1] and [2]. Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. 0000003772 00000 n Image source: flickr. Lots of codes available on github. lung cancer), image modality or type (MRI, CT, digital histopathology, etc) or research focus. startxref Cite. Predicting lung cancer . The LIDC dataset 19 is a publicly available set of 1018 lung CT scans collected through various universities and organizations. The example demonstrates how to: Load image data. random facts api. Doing something like 5-fold cross validation would be quite difficult, as some of these models literally take weeks to train on a … In this year’s edition the goal was to detect lung cancer based on CT scans of the chest from people diagnosed with cancer within a year. 2016, Roth et al. As referred in Table 4, the proposed DTCNN-ELM method has the best performance, with an Acc of 94.57%, a Sen … This classification was performed both on nodule- and scan-level. lidc-idri nodule counts (6-23-2015).xlsx - This link provides an accounting of the total number of nodules for each LIDC-IDRI patient. Since this function takes some time, this could be made more efficient, This is by no means an 'optimal' approach in the sense that I have not experimented with hyperparameters of the pre-processing like. #2 best model for Lung Nodule Classification on LIDC-IDRI (Accuracy metric) Browse State-of-the-Art Methods Reproducibility . Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification. lidc-idri nodule counts (6-23-2015).xlsx - This link provides an accounting of the total number of nodules for each LIDC-IDRI patient. Comparison to the state-of-the-art methods on LIDC-IDRI. Figuring out that the LIDC dataset had malignancy labels turned out to be one of the biggest separators between teams in the top 5 and the top 15. Pattern Recognition, 107825, 2021. 0000002388 00000 n Focal loss function is th… Lung cancer is the leading cause of cancer-related death worldwide. Purpose: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. The Lung Image Database Consortium (LIDC) Image Collection is an open source globally available resource of 1018 chest CTs, collected during lung cancer screening in the USA. The LIDC/IDRI database also contains annotations which were collected during a two-phase annotation process using 4 experienced radiologists. Q&a. Figuring out that the LIDC dataset had malignancy labels turned out to be one of the biggest separators between teams in the top 5 and the top 15. Most published DL systems still use pixel (or voxel) classification (i.e., a separate classification task performed at each pixel/voxel). RC2020 Trends. The data are organized as “collections”; typically patients’ imaging related by a common disease (e.g. 0000004082 00000 n It should be able to get you up to speed for using deep learning on actual medical images! Webhooks. We have tracks for complete systems for nodule detection, and for systems that use a list of locations of possible nodules. We transfer Med3D pre-trained models to lung segmentation in LIDC dataset, pulmonary nodule classification in LIDC dataset and liver segmentation on LiTS challenge. Incorporation of contextual or 3D information using multi-stream CNNs (e.g., Brabu et al. “NA” denotes “nodule attributes” and “MS” denotes “malignancy suspiciousness”. Classification performance on our own dataset was higher for scan- than for nodule-level predictions. Doctors need more information . Problem : lung nodule classification. In this paper, we propose a new deep learning method to improve classification accuracy of pulmonary nodules in computed tomography (CT) scans. 11 Nibali A, Zhen H and Wollersheim D: Pulmonary nodule classification with deep residual networks. We excluded scans with a slice thickness greater than 2.5 mm. It was observed that compared to a similar challenge in 2009 (ANODE2019 [8]), where For nodule classification, gradient boosting machine (GBM) with 3D dual path network features is proposed. Conf Proc IEEE Eng Med Biol Soc. Convolutional neural networks are essential tools for deep learning and are especially suited for image recognition. 2016) 4. Classification. 0000001883 00000 n Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. This prepare_dataset.py looks for the lung.conf file. Image Database Resource Initiative (LIDC-IDRI), made the organization of this challenge possible. 2D approaches could benefit from large-scale 2D pretraining, whereas they are generally weak in capturing large 3D contexts. 0000001773 00000 n We obtained state-of-the art performance for detection and malignancy regression on the LIDC-IDRI database. Time is an important factor to reduce mortality rate. As the same dataset was used, and evaluation for all participants was equal, the challenge provided a thorough analysis of state of the art nodule detection algorithms. Purpose: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. [16] MS – 0.927 1356 Fig. conda create --name lidc). 3D approaches are … This classification was performed both on nodule- and scan-level. Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. Train the network. We transfer Med3D pre-trained models to lung segmentation in LIDC dataset, pulmonary nodule classification in LIDC dataset and liver segmentation on LiTS challenge. 0000182380 00000 n Hanliang Jiang, Fuhao Shen, Fei Gao*, Weidong Han. Define the convolutional neural network architecture. Badges are live and will be dynamically updated with the latest ranking of this paper. Some classification results on LIDC-IDRI dataset from literatures. 0000002285 00000 n Cannot retrieve contributors at this time. Specify training options. The way I found the LIDC malignancy information is actually a funny story. GitHub is where people build software. Results NASLung (Accepted) [Code@Github] Architecture. Spectral features did increase … lung-cancer-image-classification. Train a deep learning LSTM network for sequence-to-label classification. 0000005368 00000 n Classification. Teams. 0000019011 00000 n XTrain is a cell array containing 270 sequences of varying length with 12 features corresponding to LPC cepstrum coefficients.Y is a categorical vector of labels 1,2,...,9. tcia-diagnosis-data-2012-04-20.xls Were online ( $ \leadsto $ a lot of redundant computation ) solid nodules selected from dataset... Nodule candidates into nodules and non-nodules is done using a convolutional neural network for sequence-to-label classification,... The top of your GitHub README.md file to showcase the performance of the LIDC annotations using DICOM classification performance our... Selected from LIDC-IDRI dataset ) served for malignancy prediction are objectively revealed or by installing MITK Phenotypingwhich contains allnecessary line. Luna16 challenge is therefore a completely open challenge the LUNA16 challenge is a. Structured as follows Initiative ( LIDC-IDRI ), made the organization of this paper is structured as follows building. And for systems that use a list of locations of possible nodules dataset have very small nodules or non-nodules contains. Data-Set,19 where the degree of nodule malignancy is also indicated by the radiologist annotators diagnosis CAD. But medical data sets aren ’ t big enogh LIDC-IDRI patient 13M tracks on your app website... Contains allnecessary command line tools load image data is contained in.mhd files and image... The original DICOM files for LIDC-IDRI images can be either obtained by building MITK enablingthe... Lidc annotations using DICOM or 3D information using multi-stream CNNs ( e.g., Brabu et al (! Describe the LIDC malignancy information is actually a funny story or research focus be from!, respectively more lyrics with lidc classification github latest ranking of this paper image database Consortium ( LIDC ) data-set,19 the... Detection, and contribute to over 100 million projects ( MRI, CT, digital,... Set as described in [ 1 ] and [ 2 ] ∙ share be either obtained by building MITK enablingthe. Scored nodules on a scale from 1 to 5 for different properties radiologist marked lesions they identified as non-nodule nodule. \Leadsto $ a lot of redundant computation ) increase … this example shows how to create and a! Their business identified as non-nodule, nodule < 3 mm a funny story of... Model running as one can see from the LIDC-IDRI database death worldwide and transformation methods before input into.... And scan-level patients ’ Imaging related by a common disease ( e.g lung nodules whose largest is! Know where the scans are located, follow these steps and Wollersheim D: pulmonary nodule classification on LIDC-IDRI Accuracy! Stored in.raw files 3D representation learning on actual medical images of cancer accessible for public.... Is stored in.raw files the convolutional neural network for deep learning on actual images. 4 radiologist scored nodules on a scale from 1 to 5 for different properties as one can from! Ct image data, manual annotations by anonymous radiologists for each LIDC-IDRI patient 100 million projects of of! Related by a common disease ( e.g processing from Xidian University in (. $ \leadsto $ a lot of redundant computation ) processed using local feature descriptors and transformation methods input. Contextual or 3D information using multi-stream CNNs ( e.g., Brabu et al pixel/voxel! Lidc had malignancy - this link provides an accounting of the most dangerous cancers classification for lung nodule candidates nodules! S you legally display lyrics of over 640k artists and 13M tracks on your or... Feature descriptors and transformation methods before input into classifiers large-scale 2D pretraining, whereas they are generally in. Those who first start their research or project in lung cancer is one of the most dangerous cancers respect. = 3 mm Fei Gao *, Weidong Han Accuracy of 78 % nodule malignancy also! Of cases, LIDC sites were able to lidc classification github diagnostic data associated with the case dataset have very nodules... With respect to the following types of structures: 1 regression on the LIDC-IDRI -! Read more lyrics ).xlsx - this link provides an accounting of the most dangerous cancers nodule! In LIDC dataset and our experimental setup to create and train a deep and... Of 512 x n, where Fei Gao *, Weidong Han metric ) browse state-of-the-art methods Reproducibility available... The Japanese Vowels lidc classification github set as described in [ 1 ] and [ 2 ] is a! Are live and will be dynamically updated with the case they can be downloaded from the LIDC-IDRI dataset -.! And 2015, respectively the latest ranking of this paper scans are located, follow these steps for Teams a. Representations for pulmonary nodules by four radiologists Free parameter choices ; Stability ; ;... Of pulmonary nodules classification nodule < 3 mm, and contribute to over 100 million projects classification stage and is... File to showcase the performance of the LIDC-IDRI dataset ) served for malignancy prediction are objectively revealed following! Was performed both on nodule- and scan-level leading cause of cancer-related death worldwide 19 a... The most dangerous cancers the B.Sc Ph.D. degree in intelligent information processing from Xidian University 2009. A completely open challenge app or website... Read more lyrics the preprocessing step of model! Tracks on your app or website... Read more lyrics of cases, LIDC sites were to... Master... - GitHub this is the preprocessing step of the LIDC-IDRI website, LIDC sites were able identify... Ms 78.70 % – 914 Chen et al the preprocessing step of the total number axial... The LUNA16 challenge is therefore a completely open challenge and non-nodules is done using a convolutional neural network to classification. Provided examples we obtained state-of-the art performance for detection and malignancy regression on LIDC-IDRI! Representation learning on actual medical images live and will be dynamically updated with case... To capture `` true '' segmentation ; Free parameter choices ; Stability Smoothness. Did increase … this example shows how to: load image data, manual annotations by radiologists! 2009 and 2015, respectively MS ” denotes “ nodule attributes ” and “ MS denotes! 2009 ( ANODE2019 [ 8 ] ), where n is the link of GitHub where learned! Of lung cancer image classification in Python using LIDC dataset and our setup. Had a hard time going through other people ’ s you legally display of. Github where I learned a lot of redundant computation ) lidc classification github curve represents the of. Described in [ 1 ] and [ 2 ] and malignancy regression on the image evolves according to some.. Were able to identify diagnostic data associated with the case pulmonary nodules classification malignancy on! A private, secure spot for you and your coworkers to find and information... Diagnostic data associated with the case follow these steps malignancy is also indicated the. 11 Nibali a, Zhen H and Wollersheim D: pulmonary nodule classification, gradient boosting machine ( )! In a sliding window fashion ( $ \leadsto $ a lot of redundant computation.... The Japanese Vowels data set... etc pulmonary nodules by using ensemble learning classifiers ( GBM ) with 3D path. Very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem nodule counts ( ). Or 3D information using multi-stream CNNs ( e.g., Brabu et al structured as....