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changed, 329 insertions(+) create mode 100644 joss.04691/10.21105.joss.04691.crossref.xml diff --git a/joss.04691/10.21105.joss.04691.crossref.xml b/joss.04691/10.21105.joss.04691.crossref.xml new file mode 100644 index 0000000000..9aa4d990ee --- /dev/null +++ b/joss.04691/10.21105.joss.04691.crossref.xml @@ -0,0 +1,329 @@ + + + + 20221009T080619-161200c060bee2e587ee0cf89638fa3922b9b500 + 20221009080619 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org/ + + + + + 10 + 2022 + + + 7 + + 78 + + + + Volume Segmantics: A Python Package for Semantic +Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning +Models + + + + Oliver N. F. + King + https://orcid.org/0000-0002-6152-7207 + + + Dimitrios + Bellos + https://orcid.org/0000-0002-8015-3191 + + + Mark + Basham + https://orcid.org/0000-0002-8438-1415 + + + + 10 + 09 + 2022 + + + 4691 + + + 10.21105/joss.04691 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.7143363 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/4691 + + + + 10.21105/joss.04691 + https://joss.theoj.org/papers/10.21105/joss.04691 + + + https://joss.theoj.org/papers/10.21105/joss.04691.pdf + + + + + + ImageNet Large Scale Visual Recognition +Challenge + Russakovsky + International Journal of Computer +Vision + 3 + 115 + 10.1007/s11263-015-0816-y + 1573-1405 + 2015 + Russakovsky, O., Deng, J., Su, H., +Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., +Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet Large +Scale Visual Recognition Challenge. International Journal of Computer +Vision, 115(3), 211–252. +https://doi.org/10.1007/s11263-015-0816-y + + + One Network to Segment Them All: A General, +Lightweight System for Accurate 3D Medical Image +Segmentation + Perslev + Medical Image Computing and Computer Assisted +Intervention – MICCAI 2019 + 10.1007/978-3-030-32245-8_4 + 978-3-030-32245-8 + 2019 + Perslev, M., Dam, E. B., Pai, A., +& Igel, C. (2019). One Network to Segment Them All: A General, +Lightweight System for Accurate 3D Medical Image Segmentation. In D. +Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P.-T. Yap, +& A. Khan (Eds.), Medical Image Computing and Computer Assisted +Intervention – MICCAI 2019 (pp. 30–38). Springer International +Publishing. +https://doi.org/10.1007/978-3-030-32245-8_4 + + + Albumentations: Fast and Flexible Image +Augmentations + Buslaev + Information + 2 + 11 + 10.3390/info11020125 + 2078-2489 + 2020 + Buslaev, A., Iglovikov, V. I., +Khvedchenya, E., Parinov, A., Druzhinin, M., & Kalinin, A. A. +(2020). Albumentations: Fast and Flexible Image Augmentations. +Information, 11(2). +https://doi.org/10.3390/info11020125 + + + U-Net Segmentation Methods for +Variable-Contrast XCT Images of Methane-Bearing Sand Using Small +Training Datasets + Alvarez-Borges + 10.1002/essoar.10506807.2 + 2022 + Alvarez-Borges, F. J., King, O. N. +F., Madhusudhan, B. N., Connolley, T., Basham, M., & Ahmed, S. I. +(2022). U-Net Segmentation Methods for Variable-Contrast XCT Images of +Methane-Bearing Sand Using Small Training Datasets. Earth; Space Science +Open Archive. +https://doi.org/10.1002/essoar.10506807.2 + + + A massively multi-scale approach to +characterizing tissue architecture by synchrotron micro-CT applied to +the human placenta + Tun + Journal of The Royal Society +Interface + 179 + 18 + 10.1098/rsif.2021.0140 + 2021 + Tun, W. M., Poologasundarampillai, +G., Bischof, H., Nye, G., King, O. N. F., Basham, M., Tokudome, Y., +Lewis, R. M., Johnstone, E. D., Brownbill, P., Darrow, M., & +Chernyavsky, I. L. (2021). A massively multi-scale approach to +characterizing tissue architecture by synchrotron micro-CT applied to +the human placenta. Journal of The Royal Society Interface, 18(179), +20210140. https://doi.org/10.1098/rsif.2021.0140 + + + Segmentation models pytorch + Yakubovskiy + GitHub repository + 2020 + Yakubovskiy, P. (2020). Segmentation +models pytorch. In GitHub repository. GitHub. +https://github.com/qubvel/segmentation_models.pytorch + + + SuRVoS 2: Accelerating Annotation and +Segmentation for Large Volumetric Bioimage Workflows Across Modalities +and Scales + Pennington + Frontiers in Cell and Developmental +Biology + 10 + 10.3389/fcell.2022.842342 + 2296-634X + 2022 + Pennington, A., King, O. N. F., Tun, +W. M., Ho, E. M. L., Luengo, I., Darrow, M. C., & Basham, M. (2022). +SuRVoS 2: Accelerating Annotation and Segmentation for Large Volumetric +Bioimage Workflows Across Modalities and Scales. Frontiers in Cell and +Developmental Biology, 10. +https://doi.org/10.3389/fcell.2022.842342 + + + SuRVoS2 + Pennington + GitHub repository + 2018 + Pennington, A., King, O. N. F., +Luengo, I., & Basham, M. (2018). SuRVoS2. In GitHub repository. +GitHub. +https://github.com/DiamondLightSource/SuRVoS2 + + + Multi-planar U-net + Perslev + GitHub repository + 2019 + Perslev, M., & Igel, C. (2019). +Multi-planar U-net. In GitHub repository. GitHub. +https://github.com/perslev/MultiPlanarUNet + + + CTSegNet + Tekawade + GitHub repository + 2020 + Tekawade, A., & Igel, C. (2020). +CTSegNet. In GitHub repository. GitHub. +https://github.com/aniketkt/CTSegNet + + + Pytorch-3dunet + Wolny + GitHub repository + 2019 + Wolny, A. (2019). Pytorch-3dunet. In +GitHub repository. GitHub. +https://github.com/wolny/pytorch-3dunet + + + DeepEM + Lee + GitHub repository + 2018 + Lee, K., & Turner, N. L. (2018). +DeepEM. In GitHub repository. GitHub. +https://github.com/seung-lab/DeepEM + + + PyTorch connectomics: A scalable and flexible +segmentation framework for EM connectomics + Lin + arXiv preprint +arXiv:2112.05754 + 10.48550/arXiv.2112.05754 + 2021 + Lin, Z., Wei, D., Lichtman, J., & +Pfister, H. (2021). PyTorch connectomics: A scalable and flexible +segmentation framework for EM connectomics. arXiv Preprint +arXiv:2112.05754. +https://doi.org/10.48550/arXiv.2112.05754 + + + UNI-EM: An Environment for Deep Neural +Network-Based Automated Segmentation of Neuronal Electron Microscopic +Images + Urakubo + Scientific Reports + 1 + 9 + 10.1038/s41598-019-55431-0 + 2045-2322 + 2019 + Urakubo, H., Bullmann, T., Kubota, +Y., Oba, S., & Ishii, S. (2019). UNI-EM: An Environment for Deep +Neural Network-Based Automated Segmentation of Neuronal Electron +Microscopic Images. Scientific Reports, 9(1), 19413. +https://doi.org/10.1038/s41598-019-55431-0 + + + PyTorch connectomics + Lin + GitHub repository + 2019 + Lin, Z., Lu, Y., Belhamissi, M., +Banerjee, A., Lauenburg, L., Swaroop, K. K., Wei, D., & Pfister, H. +(2019). PyTorch connectomics. In GitHub repository. GitHub. +https://github.com/zudi-lin/pytorch_connectomics + + + Neutorch + Wu + GitHub repository + 2021 + Wu, J. (2021). Neutorch. In GitHub +repository. GitHub. +https://github.com/flatironinstitute/neutorch + + + PyTorch: An imperative style, +high-performance deep learning library + Paszke + Advances in neural information processing +systems 32 + 2019 + Paszke, A., Gross, S., Massa, F., +Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, +N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, +M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S. +(2019). PyTorch: An imperative style, high-performance deep learning +library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché-Buc, E. +Fox, & R. Garnett (Eds.), Advances in neural information processing +systems 32 (pp. 8024–8035). Curran Associates, Inc. +http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf + + + + + + From e07b16c9b987bc3d36599f64a50f17d302653f79 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Sun, 9 Oct 2022 09:06:34 +0100 Subject: [PATCH 3/4] Creating 10.21105.joss.04691.jats --- joss.04691/10.21105.joss.04691.jats | 623 ++++++++++++++++++++++++++++ 1 file changed, 623 insertions(+) create mode 100644 joss.04691/10.21105.joss.04691.jats diff --git a/joss.04691/10.21105.joss.04691.jats b/joss.04691/10.21105.joss.04691.jats new file mode 100644 index 0000000000..d7f23b5964 --- /dev/null +++ b/joss.04691/10.21105.joss.04691.jats @@ -0,0 +1,623 @@ + + +
      + + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +4691 +10.21105/joss.04691 + +Volume Segmantics: A Python Package for Semantic +Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning +Models + + + +0000-0002-6152-7207 + +King +Oliver N. F. + + +* + + +0000-0002-8015-3191 + +Bellos +Dimitrios + + + + +0000-0002-8438-1415 + +Basham +Mark + + + + + + +Diamond Light Source Ltd., Harwell Science and Innovation +Campus, Didcot, Oxfordshire, UK + + + + +Rosalind Franklin Institute, Harwell Science and Innovation +Campus, Didcot, Oxfordshire, UK + + + + +* E-mail: + + +20 +7 +2022 + +7 +78 +4691 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +segmentation +deep learning +volumetric +images +pre-trained + + + + + + Summary +

      Segmentation of 3-dimensional (3D, volumetric) images is a widely + used technique that allows interpretation and quantification of + experimental data collected using a number of techniques (for example, + Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Electron + Tomography (ET)). Although the idea of semantic segmentation is a + relatively simple one, giving each pixel a label that defines what it + represents (e.g cranial bone versus brain tissue); due to the + subjective and laborious nature of the manual labelling task coupled + with the huge size of the data (multi-GB files containing billions of + pixels) this process is often a bottleneck in imaging workflows. In + recent years, deep learning has brought models capable of fast and + accurate interpretation of image data into the toolbox available to + scientists. These models are often trained on large image datasets + that have been annotated at great expense. In many cases however, + scientists working on novel samples and using new imaging techniques + do not yet have access to large stores of annotated data. To overcome + this issue, simple software tools that allow the scientific community + to create segmentation models using relatively small amounts of + training data are required. Volume Segmantics + is a Python package that provides a command line interface (CLI) as + well as an Application Programming Interface (API) for training + 2-dimensional (2D) PyTorch + (Paszke + et al., 2019) deep learning models on small amounts of + annotated 3D image data. The package also enables applying these + models to new (often much larger) 3D datasets to speed up the process + of semantic segmentation.

      +
      + + Statement of need +

      Volume Segmantics harnesses the availability + of 2-dimensional encoders which have been pre-trained on huge + databases such as ImageNet + (Russakovsky + et al., 2015). This provides two main advantages, namely (i) it + reduces the time and resource taken to train models — only fine-tuning + is required and (ii) it prevents over-fitting the models when training + on small datasets. These models of various architectures are included + from the segmentation-models-pytorch repository + (Yakubovskiy, + 2020). In order to increase the accuracy of the models, + augmentations of the data are made during training, both via ‘slicing’ + the 3D data in planes perpendicular to the three orthogonal axes + + + ((x,y),(x,z),(y,z)) + and by using the library Albumentations + (Buslaev + et al., 2020). Additionally, user configuration for training is + kept to a minimum by starting with a reliable default set of + parameters and by automatically choosing the model learning rate. If + adjustments to model architecture, encoder type, loss function or + training epochs are required; these can be made by editing a YAML + file.

      +

      Even though these 2D models are quicker to train and require fewer + computational resources than their 3D counterparts + (Alvarez-Borges + et al., 2022), when predicting a segmentation for a volume, the + lack of 3D context available to these models can lead to striping + artifacts in the 3D output, especially when viewed in planes other + than the one used for prediction. To overcome this, a multi-axis + prediction method is used, and the multiple predictions are merged by + using maximum probability voting. It is hoped that in the future other + merging techniques will be included such as fusion models + (Perslev + et al., 2019). A schematic of the training and prediction + processes performed by the Volume Segmantics + package can be seen in + Figure 1.

      + +

      A schematic diagram showing the model training and + segmentation prediction processes performed by the + Volume Segmantics + package.

      + +
      + + State of the field +

      Currently there are a number of other software implementations + available for segmentation of 3D data. Some of these also use 2D + networks and combine prediction outputs to 3D, for example the + Multi-Planar U-Net package + (Perslev + & Igel, 2019) and the CTSegNet + package + (Tekawade + & Igel, 2020). However, neither of these packages allows + the use of pre-trained encoders or multiple model architectures. In + addition, the general purpose pytorch-3dunet + package + (Wolny, + 2019) exists to allow training a 3D U-Net on image data, + again without the time and resource advantages of pre-trained 2D + networks.

      +

      In the field of connectomics, several packages + (Lee + & Turner, 2018; + Lin + et al., 2021; + Urakubo + et al., 2019; + Wu, + 2021) enable the segmentation of structures within the brain, + often from electron microscopy data, these could in principle be + used in a subject and method-agnostic manner similarly to + Volume Segmantics. One member of this set, + the package pytorch-connectomics, + (Lin + et al., 2019) allows training of 2D and 3D networks for + segmentation of 3D image data as well as customisable strategies for + data augmentation and multi-task and semi-supervised learning. + Despite the versatility of this software, its deliberate focus on + connectomics which is essential for effectiveness in this complex + field, means that there are added levels of complexity for the + generalist user. This specialisation also means that there is only + one pre-trained 2D model architecture available, and some of the + configuration and command-line options are context specific.

      +
      + + Real-world usage +

      During development of Volume Segmantics, + the software was used to fine-tune pre-trained U-Net models on small + amounts of annotated data in order to investigate the structures + that interface maternal and fetal blood volumes in human placental + tissue + (Tun + et al., 2021). In this study, expert annotation of volumes of + size + + 2563 + and + + 3843 + were sufficient to create two models that gave accurate segmentation + of two much larger synchrotron X-ray CT (SXCT) datasets + ( + + 2520×2520×2120 + pixels). In a completely different context, SXCT datasets were + collected on a soil system in which methane bubbles were forming in + brine amongst sand particles. The utility of a pre-trained 2D U-Net + was investigated to segment these variable-contrast image volumes in + comparison to a 3D U-Net with no prior training + (Alvarez-Borges + et al., 2022). In this case, the training data ranged in size + from + + 3843 + pixels to + + 5723. + As well as requiring less time to train than a 3D U-Net, the + pre-trained 2D network provided more accurate segmentation + results.

      +
      + + The API +

      The API provided with the package allows segmentation models to + be trained and used in other contexts. For example, + Volume Segmantics has recently been + integrated into SuRVoS2 + (Pennington + et al., 2018, + 2022), + a client-server application with a GUI for annotating volumetric + data. SuRVoS2 can be used to create the initial small region of + interest (ROI) labels needed by + Volume Segmantics, this is achieved by using + machine learning models (e.g. random forests) which are trained + through ‘scribbles’ drawn on the data. It is hoped that scientists + using our synchrotron facility and beyond will be able to train and + use their own deep learning models using this interface to the + library. These models can then be used to segment data during their + time using the synchrotron and also when back at their home + institution. In addition, it is hoped that the scientific community + will use and extend Volume Segmantics for + their own purposes.

      +
      +
      + + Acknowledgements +

      We would like to acknowledge helpful discussions with Avery + Pennington, Sharif Ahmed, Fernando Alvarez-Borges and Michele Darrow + during the development of this project. Additional thanks to Luis + Perdigao for inspiration and icons for the schematic figure.

      +
      + + + + + + + RussakovskyOlga + DengJia + SuHao + KrauseJonathan + SatheeshSanjeev + MaSean + HuangZhiheng + KarpathyAndrej + KhoslaAditya + BernsteinMichael + BergAlexander C. + Fei-FeiLi + + ImageNet Large Scale Visual Recognition Challenge + International Journal of Computer Vision + 201512 + 20200521 + 115 + 3 + 1573-1405 + https://doi.org/10.1007/s11263-015-0816-y + 10.1007/s11263-015-0816-y + 211 + 252 + + + + + + PerslevMathias + DamErik Bjørnager + PaiAkshay + IgelChristian + + One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation + Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 + + ShenDinggang + LiuTianming + PetersTerry M. + StaibLawrence H. + EssertCaroline + ZhouSean + YapPew-Thian + KhanAli + + Springer International Publishing + Cham + 2019 + 978-3-030-32245-8 + 10.1007/978-3-030-32245-8_4 + 30 + 38 + + + + + + BuslaevAlexander + IglovikovVladimir I. + KhvedchenyaEugene + ParinovAlex + DruzhininMikhail + KalininAlexandr A. + + Albumentations: Fast and Flexible Image Augmentations + Information + 2020 + 11 + 2 + 2078-2489 + https://www.mdpi.com/2078-2489/11/2/125 + 10.3390/info11020125 + + + + + + Alvarez-BorgesFernando Jesus + KingOliver N. F. + MadhusudhanB. N. + ConnolleyThomas + BashamMark + AhmedSharif I. + + U-Net Segmentation Methods for Variable-Contrast XCT Images of Methane-Bearing Sand Using Small Training Datasets + Earth; Space Science Open Archive + 202207 + 20220725 + http://www.essoar.org/doi/10.1002/essoar.10506807.2 + 10.1002/essoar.10506807.2 + + + + + + TunW. M. + PoologasundarampillaiG. + BischofH. + NyeG. + KingO. N. F. + BashamM. + TokudomeY. + LewisR. M. + JohnstoneE. D. + BrownbillP. + DarrowM. + ChernyavskyI. L. + + A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta + Journal of The Royal Society Interface + 202106 + 20220721 + 18 + 179 + https://royalsocietypublishing.org/doi/10.1098/rsif.2021.0140 + 10.1098/rsif.2021.0140 + 20210140 + + + + + + + YakubovskiyPavel + + Segmentation models pytorch + GitHub repository + GitHub + 2020 + https://github.com/qubvel/segmentation_models.pytorch + + + + + + PenningtonAvery + KingOliver N. F. + TunWin Min + HoElaine M. L. + LuengoImanol + DarrowMichele C. + BashamMark + + SuRVoS 2: Accelerating Annotation and Segmentation for Large Volumetric Bioimage Workflows Across Modalities and Scales + Frontiers in Cell and Developmental Biology + 2022 + 10 + 2296-634X + https://www.frontiersin.org/articles/10.3389/fcell.2022.842342 + 10.3389/fcell.2022.842342 + + + + + + PenningtonAvery + KingOliver N. F. + LuengoImanol + BashamMark + + SuRVoS2 + GitHub repository + GitHub + 2018 + https://github.com/DiamondLightSource/SuRVoS2 + + + + + + PerslevMathias + IgelChristian + + Multi-planar U-net + GitHub repository + GitHub + 2019 + https://github.com/perslev/MultiPlanarUNet + + + + + + TekawadeAniket + IgelChristian + + CTSegNet + GitHub repository + GitHub + 2020 + https://github.com/aniketkt/CTSegNet + + + + + + WolnyAdrian + + Pytorch-3dunet + GitHub repository + GitHub + 2019 + https://github.com/wolny/pytorch-3dunet + + + + + + LeeKisuk + TurnerNicholas L. + + DeepEM + GitHub repository + GitHub + 2018 + https://github.com/seung-lab/DeepEM + + + + + + LinZudi + WeiDonglai + LichtmanJeff + PfisterHanspeter + + PyTorch connectomics: A scalable and flexible segmentation framework for EM connectomics + arXiv preprint arXiv:2112.05754 + 2021 + 10.48550/arXiv.2112.05754 + + + + + + UrakuboHidetoshi + BullmannTorsten + KubotaYoshiyuki + ObaShigeyuki + IshiiShin + + UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images + Scientific Reports + 201912 + 20220914 + 9 + 1 + 2045-2322 + https://www.nature.com/articles/s41598-019-55431-0 + 10.1038/s41598-019-55431-0 + 19413 + + + + + + + LinZudi + LuYuhao + BelhamissiMourad + BanerjeeAtmadeep + LauenburgLeander + SwaroopK. Krishna + WeiDonglai + PfisterHanspeter + + PyTorch connectomics + GitHub repository + GitHub + 2019 + https://github.com/zudi-lin/pytorch_connectomics + + + + + + WuJingpeng + + Neutorch + GitHub repository + GitHub + 2021 + https://github.com/flatironinstitute/neutorch + + + + + + PaszkeAdam + GrossSam + MassaFrancisco + LererAdam + BradburyJames + ChananGregory + KilleenTrevor + LinZeming + GimelsheinNatalia + AntigaLuca + DesmaisonAlban + KopfAndreas + YangEdward + DeVitoZachary + RaisonMartin + TejaniAlykhan + ChilamkurthySasank + SteinerBenoit + FangLu + BaiJunjie + ChintalaSoumith + + PyTorch: An imperative style, high-performance deep learning library + Advances in neural information processing systems 32 + + WallachH. + LarochelleH. + BeygelzimerA. + dAlché-BucF. + FoxE. + GarnettR. + + Curran Associates, Inc. + 2019 + http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf + 8024 + 8035 + + + + +
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