dataesr/scientific-paragraphs-categorization huggingface dataset:
In our framework, SLANT-27 (docker) obtained slightly lower results than the ones published in the original paper  [18]. This may come from hardware and environment differences. In addition, we analyzed the performance of the methods according to the dataset. Mean Dice coefficients obtained on each testing dataset (i. e. , OASIS, CANDI and Colin27) are provided in Table  4. As expected, all the methods performed better on adult scans from the OASIS dataset since the training dataset comes from the same cohort. Moreover, all the images were acquired with the same protocol on the same scanner for this dataset. First, we can note the good performance of JLF compared to U-Net and SLANT-8. Moreover, when considering methods trained with 45 training, AssemblyNet outperformed U-Net by 8. 2 pp, JLF by 4.
4 pp and SLANT-27 by 1. 2 pp of mean Dice. When considering all the methods, AssemblyNet with SSL obtained significantly better Dice than SLANT-27 (docker) trained on 5111+45 images. It has to be noted that in  [18] , the authors have shown that the SLANT-27 method significantly outperformed U-Net, JLF and SLANT-8. Finally, on the OASIS images, using SSL did not significantly improve the AssemblyNet results. On the high-resolution Colin27 image, we also observed an important decrease of performance for all the methods except for AssemblyNet and SLANT-27 trained on 5111+45 images. As for CANDI dataset, AssemblyNet obtained the best segmentation accuracy with or without SSL on this dataset. As an illustration, Figure  2  shows the segmentations of the central slides in the native space obtained by SLANT-27 (docker) and AssemblyNet with SSL on the first subject of the testing dataset (ID=1120_3).
Robustness to disease effects The last part of our validation is dedicated to robustness to disease effects. To this end, we compared SLANT-27 (docker) and AssemblyNet on the pathological dataset composed of CN subjects and AD patients. First, we estimated the mean Dice for both groups (see Table  6 ). For the CN group, AssemblyNet with SSL obtained a significantly better Dice than both other methods. For the AD group, we obtained similar results although the improvement obtained by AssemblyNet with SSL was higher for AD group (2. 2 pp) than for the CN group (1. 7 pp) compared to SLANT-27 (docker). In addition, we compared the accuracy between CN and AD groups for the three methods. We found no significant differences between groups for all the methods although this difference was almost significant for SLANT-27 docker (p=0.
e. , unseen acquisition protocol, age period and pathology). Compared to previous methods using larger auxiliary datasets labelled with classical tools  [14] ,  [18] , the proposed SSL framework is more practical in term of computational time and resources. However, SSL is currently receiving special attention in deep learning community. Consequently new paradigms should be considered  [41] ,  [42]. Afterwards, we compared our AssemblyNet with state-of-the-art methods. We demonstrated the high performance of our method in terms of segmentation accuracy and computational time. First, these experiments demonstrated the advantage of using several CNNs to segment the whole brain since SLANT-27 and AssemblyNet clearly outperformed the use of a single U-Net. Moreover, these results showed that using a larger number of simpler CNNs within a multiscale framework is an efficient strategy.
While for OASIS we obtained Dice higher than intra-expert consistency, the accuracy on the CANDI dataset is still limited. This can come from several factors such as the lower image quality (e. g. , more motion artifacts in child images) or the larger distance between adult training dataset and this child dataset. These points should be deeper investigated in future whole brain segmentation methods. In terms of computational time, our method could be further improved by using several GPUs. At training time, once the first U-Net is trained, several following U-Nets can be trained in parallel despite our transfer learning strategy. At testing time, AssemblyNet can be fully parallelized and thus the processing time could be drastically reduced using multiple GPUs. In addition, we investigated the scan-rescan consistency of the proposed method.
We showed that the intra-method consistency of our method reached 92. 8% while intrarater consistency was limited to 76. 8%. This result clearly demonstrates that our automatic method segments the whole brain in a more consistent manner than a human expert. Moreover, we show that the expert-method consistency obtained with our method is not significantly lower than the intra-rater consistency, which is an encouraging result. However, these results were obtained using only 4 scan-rescan subjects. In addition, the Morphometric dataset does not contain material to evaluate method reproducibility (same image segmented twice by the same expert). Such data would have been useful to evaluate if automatic methods had reached human variability. Finally, these results raise the question of using human segmentation as "gold standard" with a consistency lower than 80%.
Comparison with state-of-the-art methods on the different testing datasets (5 adult scans from OASIS, 13 child scans from CANDI child and the high-resolution Colin27 image based on scans average). Using Wilcoxon tests * indicates a significant lower Dice compared to AssemblyNet with semi-supervised learning (i. e. , 360 + FT 45) when compared to SLANT-27 (docker) and AssemblyNet. On child scans from the CANDI dataset acquired with a different protocol, we can first note a dramatic drop in performance for all the methods except for AssemblyNet with and without SSL and SLANT-27 trained on 5111+45 images. Moreover, when considering methods trained with 45 training, AssemblyNet outperformed U-Net by 19. 7 pp, JLF by 19. 2 pp and SLANT-27 by 9 pp of mean Dice. This demonstrates the robustness and the generalization capability of the proposed framework with regards to unseen acquisition protocol and ages.
Reliability study on the scan-rescan datasets (3 adult scans from OASIS and one scan of a patient with AD from ADNI). The intra-method consistency is the mean Dice between the automatic segmentations obtained on the scan and rescan images. The Expert-Method consistency is the meanDice between the automatic segmentation on the rescan image and the manual segmentation of the scan image. The Dice coefficients obtained on the scan and the rescan images are averaged. The intra-rater consistency is the mean Dice between the manual segmentations obtained on the scan and rescan images. Using Wilcoxon tests * indicates a significant lower Dice compared to AssemblyNet with semisupervised learning (i. e. , 360 + FT 45).
Methods comparison on the pathological dataset (29 scans from the ADNI dataset including 15 CN and 14 patients with AD). * indicates a significant lower Dice compared to AssemblyNet with semisupervised learning (i. e. , 360 + FT 45) using Wilcoxon tests. In this work, we presented a novel whole brain segmentation framework based on a large number of 3D CNN (i. e. , 250 U-Nets) called AssemblyNet. First, we showed that the use of Atlas prior, nearest neighbor transfer learning and multiscale cascade of Assemblies enable to improve global segmentation accuracy. In further work alternative options could be investigated. First, atlas prior could be replaced by fast multi-atlas prior. Thanks to nonlinear registration methods based on deep learning, such prior is no more too expensive. Moreover, more advanced communication between assembly members should be investigated.
Acknowledgements This work benefited from the support of the project DeepvolBrain of the  French National Research Agency  ( ANR-18-CE45-0013 ). This study was achieved within the context of the  Laboratory of Excellence TRAIL   ANR-10-LABX-57  for the  BigDataBrain  project. Moreover, we thank the  Investments for the future Program IdEx Bordeaux  ( ANR-10-IDEX-03-02 ,  HL-MRI  Project),  Cluster of excellence CPU  and the  CNRS/INSERM  for the  DeepMultiBrain  project. This study has been also supported by the  DPI2017-87743-R grant  from the  Spanish Ministerio de Economia, Industria Competitividad. The authors gratefully acknowledge the support of  NVIDIA Corporation  with their donation of the TITAN Xp GPU used in this research. Moreover, this work is based on multiple samples. We wish to thank all investigators of these projects who collected these datasets and made them freely accessible.
The C-MIND data used in the preparation of this article were obtained from the  C-MIND Data Repository  (accessed in Feb 2015) created by the  C-MIND study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by  Cincinnati Children's Hospital Medical Center  and  UCLA  and supported by the  National Institute of Child Health and Human Development  (Contract #s  HHSN275200900018C ). A listing of the participating sites and a complete listing of the study investigators can be found at https://research. cchmc. org/c-mind. The NDAR data used in the preparation of this manuscript were obtained from the  NIH -supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the  National Institutes of Health  to provide a national resource to support and accelerate research in autism.
The NDAR dataset includes data from the  NIH Pediatric MRI   Data Repository  created by the  NIH MRI Study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by the  Brain Development Cooperative Group  and supported by the  National Institute of Child Health and Human Development , the  National Institute on Drug Abuse , the  National Institute of Mental Health , and the  National Institute of Neurological Disorders and Stroke  (Contract #s  N01-HD02-3343 ,  N01-MH9-0002 , and  N01-NS-9-2314 , -2315, -2316, -2317, -2319 and -2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://pediatricmri. nih. gov/nihpd/info/participating_centers.
S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the  Foundation for the National Institutes of Health  (www. fnih. org). The grantee organization is the  Northern California Institute for Research and Education , and the study was coordinated by the  Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the  Laboratory for NeuroImaging at the University of California, Los Angeles. This research was also supported by  NIH  grants  P30AG010129 ,  K01 AG030514  and the  Dana Foundation. The OASIS data used in the preparation of this manuscript were obtained from the  OASIS  project funded by grants  P50 AG05681 ,  P01 AG03991 ,  R01 AG021910 ,  P50 MH071616 ,  U24 RR021382 ,  R01 MH56584. See http://www.
oasis-brains. org/ for more details. The AIBL data used in the preparation of this manuscript were obtained from the  AIBL  study of ageing funded by the  Common-wealth Scientific Industrial Research Organization (CSIRO ; a publicly funded government research organization),  Science Industry Endowment Fund, National Health and Medical Research Council of Australia  (project grant  1011689 ),  Alzheimer's Association, Alzheimer's Drug Discovery Foundation , and an anonymous foundation. See www. aibl. csiro. aufor further details. The ICBM data used in the preparation of this manuscript were supported by  Human Brain Project  grant  PO1MHO52176-11  (ICBM,  P. I. Dr John Mazziotta ) and  Canadian Institutes of Health Research  grant  MOP-34996. The IXI data used in the preparation of this manuscript were supported by the  U.
K. Engineering and Physical Sciences Research Council (EPSRC)   GR/S21533/02  -http://www. brain-development. org/. The ABIDE data used in the preparation of this manuscript were supported by ABIDE funding resources listed at http://fcon_1000. projects. nitrc. org/indi/abide/. ABIDE primary support for the work by  Adriana Di Martino  was provided by the  NIMH  ( K23MH087770 ) and the  Leon Levy Foundation. Primary support for the work by  Michael P. Milham  and the INDI team was provided by gifts from  Joseph P. Healy  and the  Stavros Niarchos Foundation  to the  Child Mind Institute , as well as by an  NIMH  award to MPM ( R03MH096321 ). http://fcon_1000. projects. nitrc. org/indi/abide/This manuscript reflects the views of the authors and may not reflect the opinions or views of the database providers.
The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cells' positions, shapes, trajectories or lineages, axon traces in whole brains atlases or varied intracellular protein localisations, often in multiple experimental conditions. The data mining of those geometrical objects requires a variety of mathematical and computational tools of diverse accessibility and complexity. Here we present a new Python library for quantitative 3D geometry called GeNePy3D which helps handle and mine information and knowledge from geometric data, providing a unified application programming interface (API) to methods from several domains including computational geometry, scale space methods or spatial statistics.
Introduction Bioimage informatics aims at bringing microscopy into quantitative biology, associating higher level information to pixels to answer complex biological questions. In particular machine learning based techniques  1  are easing the image analysis step, extracting geometrical objects from multidimensional images. But the next step, transforming that geometrical information into biological knowledge, involves a very diverse set of algorithmic tools in distinct communities, from spatial statistics  2, 3  to computational geometry  4, 5  or neuroinformatics  6. Similarly, the software ecosystem around geometrical data analysis is very diverse and heterogeneous, with reference algorithm implementation spread across languages (Spatstat  7  for spatial statistics in R, CGAL  8  for computational geometry in C++) or across module in python (scipy  9  for generic algorithms, anytree 10 for trees, trimesh  11  for meshes etc), a lack of generic geometric data exchange format and standard graphical tools like Fiji  12  and Icy 13 being limited in the flexibility of the analysis easily available.
To address this problem, we propose GeNePy3D 14,15 , a python library meant as a 'middleware' library to facilitate building data analysis workflows for geometrical objects by providing one convenient API for geometrical data I/O, conversion and interaction between geometrical objects and access to many common and less common algorithm. We will introduce below the architecture of the library and show one example workflow, re-analysing a published dataset of zebrafish brain neuronal traces by combining traces and brain region to extract quantitative metrics per region.
Implementation GeNePy3D is implemented in Python, taking advantage of a high-level programming language with simple syntax and many open-source packages. We reused algorithms and functions available from various recognised packages when possible, and developed our own implementation when needed, within a unique interface. Most of the packages we link to are available from the Python package Index (PyPi) and can be easily installed via Python package manager (pip). Figure  1  lists out some functions with colors denoting the package used. Standard ones includes Numpy, Scipy, or Matplotlib, more specific ones includes AnyTree for tree manipulation, TriMesh for surface manipulation or ScikitLearn for machine learning tasks. Other feature are listed as optional, as they come from harder to install or less recognized sources, including the C++ library CGAL, only partially available in Python, for generic object interaction in 3D, or the optimal transport method implemented in PyEMD.
Operation GeNePy3D works with Python 3. 6. Details of the specific software requirements, documentation including the installation instruction and Python notebooks examples can be accessed via https://genepy3d. gitlab. io. Example pipelines using GeNePy3D are run using Jupyter notebooks. To ease the use and deployment of GeNePy3D we provide ready to use docker containers at https://gitlab. com/genepy3d/genepy3d_dockers.
Use case To exemplify the use of GeNePy3D 14,15 , we reanalyzed a recently published dataset containing up to 2000 traced neurons across the whole brain of larval zebrafish  17. The authors annotated 36 symmetric regions and established a connectivity atlas for the neurons within these regions. Figure  2A  illustrates a possible workflow using GeNePy3D for reanalyzing the dataset. The inputs consist of neuronal traces in SWC formats and a 3D volume in NRRD format containing different annotated labels for the 36 brain regions. The traces are imported into GeNePy3D under Tree objects, while the regions are reconstructed into Surface objects using marching cube algorithm. Figure  2B  top illustrates the outline of the Tectum along with all neuronal traces arriving to this brain region. We then extracted branching point positions from the neuronal traces (Tree→Points), decomposed them into sections (Tree→Curves) and checked whether the branching points or curve sections lies within or outside each region (interaction with Surface).
Conclusions The advent of machine learning and large scale imaging is leading to large geometrical datasets, and GeNePy3d 14,15 aims at enabling complex analysis workflows based on those objects. But as in other aspects of bioimage informatics, the key will be for the community to work together and define common formats and structures for region of interests and geometric objects to ease the interactions between the various visualisation, data management or analysis tools, and convert raw images to biological knowledge. GeNePy3d is ready to become a component of that ecosystem. genepy, all containing entirely unrelated algorithms. I would strongly suggest coming up with a more self-descriptive and less overused name. I am no expert in software licensing, but I worry that the two-licenses solution adopted here may be unnecessarily confusing for the end-user.
Wouldn't there be a way to package the entirety of the library under a single BSD3/GPL license, or license it all under the most restrictive of the two if applicable? If having two repos under two licenses really is the only possible solution, some clear explanation of why this is so should be provided in the article. Reviewer Expertise: Bioimage analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. called genepy, all containing entirely unrelated algorithms. I would strongly suggest coming up with a more self-descriptive and less overused name. Finding a name for a project is complicated and we agree that, in retrospect, better choices most likely exist.
It originally stood for Geometry of Neuron in Python in 3D. It might be clearer when heard (something like 'geeneepaï'). We may indeed change it in the future, if it gains traction and the user and developer base expands; when going out of alpha for example. I am no expert in software licensing, but I worry that the two-licenses solution adopted here may be unnecessarily confusing for the end-user. Wouldn't there be a way to package the entirety of the library under a single BSD3/GPL license, or license it all under the most restrictive of the two if applicable? If having two repos under two licenses really is the only possible solution, some clear explanation of why this is so should be provided in the article.
Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? PartlyIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed.
License: The library is distributed as two packages for licensing reasons. The main package GeNePy3D 14 is under a BSD 3-Clause Licence, while features that necessitate linking to GPL-licensed code are distributed separately in GeNePy3D_GPL  15  , under the GNU General Public License v3. 0. The source code for the analysis of Figure  2  is available at https://gitlab. com/genepy3d/genepy3d_examples/-/tree/master/ zebrafish_atlas.
○ Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? PartlyIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed.
Software A custom LabVIEW (National Instruments) application was developed to control both the commercial OCT system, the imaging galvo, the therapy laser galvo synchronization, and the laser shutter. The software permits three distinct modes summarized in Table  1  and described below. In order to study the impact of coagulation, noise, and motion on g (2) , as well as to validate our model for motion correction, the correlation for very small values of τ was calculated. This was accomplished with the first mode called Fast mode. The imaging galvo was set to run at a 1 kHz frequency and a small scanning range while the therapy laser was left to fire continuously. The ensemble size used for calculating g (2) were ∆z = 10, ∆x = 16, ∆t = 100 in pixels or ∆z = 77. 9 µm, ∆x = 237 µm, ∆t = 100 ms in physical units.
We note that the simulated data was noise free and that the experimental data was corrected for noise. Next, the effect of motion correction on experimental data was evaluated. The OCT scan rate was set at 1 kHz over a small field of view and the therapy laser power was set to 1. 0 W. Motion was induced with a motor, as described in Sec. 3 g (2) was computed for a fixed z and x for two fixed t corresponding to during and after coagulation with the laser still on in both instances. Coagulation was assessed visually after laser exposure. In this fast mode, there is no untreated region of the sample that can be used for determining g  (2)  nc during the treatment. Instead, we assumed that the magnitude of the motion would remain approximately constant during the entire experiment. We thus estimated the motion by determining g  (2)  nc before the therapy started, and used this same value for all subsequent motion correction.