dataesr/scientific-paragraphs-categorization huggingface dataset:
Reconstruction with Xeleris™ Simulated SPECT projections integrated into the experimental Infinia patient DICOM headers for all time points post injection were successfully accepted by the vendor-specific Xeleris workstation. Reconstruction of the simulated SPECT images was performed with Xeleris™ software from GE Healthcare (v3. 0513) at ICO, Nantes, France. Images were reconstructed using the OSEM algorithm (5 iterations, 16 subsets). Attenuation correction was performed with appropriate attenuation maps derived from the CT dataset. To account for the TEW scatter correction, projections corresponding to the lower scatter of 177 Lu i. e. 178 keV ± 5% and an upper scatter of 177 Lu i. e. 241 keV ± 5% were generated from the root file as explained in Section 2. 6 and subtractive method was applied  [30].
Conclusions A realistic simulation of 177 Lu clinical datasets (patient SPECT images) was generated with realistic activities in a therapeutic context. These modelled datasets will be further circulated to different clinical departments to perform dosimetry. The dosimetric analysis of our simulated data will be performed in different clinical departments as part of the IAEA CRP project E23005. This will prove the feasibility of the DosiTest project. Further work is needed to determine if and how simulated images can be provided for the whole range of gamma camera manufacturers (GE, Siemens, Philips or Mediso). At this stage, it is not clear if the problem can be solved by transcoding the simulated data in another proprietary format, or if a specific modelling of each gamma camera is needed.
Funding OpenDose3D module in Slicer3D toolkit has been developed as a part of the  Medirad  project, which received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No. 755523. This work has been partially funded by the  ENEN  + project that has received funding from the  Euratom  research and training Work programme  2016-2017-1 #75576.
Examples of monoliths are 3D-printed in polylactic acid using fused filament fabrication technology with a layer thickness of 0. 20, 0. 12, and 0. 08 mm. The images generated from the digital model slicing are analyzed using open-source ImageJ software to obtain information about internal geometrical shape, porosity, tortuosity, grain size distribution, and hydraulic conductivities. The results show that the developed script enables designing reproducible numerical models that imitate soil structures with defined pore and grain sizes in a range between coarse sand (from 1 mm diameter) to fine gravel (up to 12 mm diameter).
The main characteristics of the model will be identified and studied according to the complexity of the phenomena in the soil and then a design approach will be achieved to define the 3D printing process. To support this design approach, one main challenge is the development of software that allows soil scientists to create their soil models according to their needs in terms of the soil structure. This software should be dedicated to scientific research and should promote data sharing and exchange across an international community. One principle of the research is to opt for an open-source system. Indeed, opensource makes the evolution of the modeling software easier  [18] [19] [20]. For years, opensource software and hardware have contributed to the modernization and improvement of agriculture.
For example, open-source software such as the statistical software R [21] is used for data analysis and GeoFIS as a decision-support tool for precision agriculture data  [22]. Open-source hardware examples are found on the notion of precision agriculture and SmartFarm by integrating open-source technologies such as smart sensors, recording devices, and drones  [23] [24] [25] [26]. In this context, the objective of this paper is to present a developed open-source toolchain that allows soil scientists to generate customizable and reproducible digital soil models, called monoliths. This open-source script is developed under the IceSL environment  [27] , which is an open-source slicer. The Section 2 introduces the need for 3D-printed monoliths for soil scientists and the script development approach.
Script Developement Methodology Figure  1  shows the main steps of the methodology for developing the script allowing for the generation of a porous structure. The following paragraphs describe each step.  The monolith design software (or script) must be open-source in orde available to the scientific community and to allow for its evolution;  The design software (or script) should provide a user-friendly interface figuration of the monolith;  The 3D printer used to produce the monoliths should preferably be open  The design and fabrication of monoliths must be replicable;  The materials used for the 3D-printing of monoliths must be non-tox fauna, and fungi;  It is preferable that the script gives the possibility to add a root system to t model (i. e. , to create a void root system).
 The L-System, or Lindenmayer system, is a common tool in plant eling that is used for creating root architecture  [31]. It will help i quired root system in the monolith model as described later As a result of this study, a trabecular structure is selected and an a ing its generation is employed in the developed script for the generatio open-cell structure. In addition, the L-System is used to generate the r monolith.
• The L-System, or Lindenmayer system, is a common tool in plant architecture modeling that is used for creating root architecture  [31]. It will help in creating the required root system in the monolith model as described later As a result of this study, a trabecular structure is selected and an algorithm supporting its generation is employed in the developed script for the generation of the monolith open-cell structure. In addition, the L-System is used to generate the root system of the monolith.
Selection of the Development Software To allow the design of the monoliths using mathematical models, it will firs essary to choose a modeling software for developing the design script. Indeed, monolith model is based on complex mathematical functions, only a developm that allows the implementation of such functions is interesting. The development software must be open-source and meet the different functionalities and specifications. Many CAD and Slicing software programs e some of them combine both functionalities. A list of selection criteria has been est in order to objectively select the most adequate software. Table  1  shows these criteria and a comparison between different software and slicers according to the The symbol "×" means that the software has this functionality (i. e. , it meets this c × × × OpenSCAD × × × IceSL × × × × × Three software packages are available offering the possibility to develop Blender is a powerful tool with interesting features; however, it is more adapted on meshes than on solids based on volume.
Selection of the Development Software To allow the design of the monoliths using mathematical models, it will first be necessary to choose a modeling software for developing the design script. Indeed, since the monolith model is based on complex mathematical functions, only a development tool that allows the implementation of such functions is interesting. The development software must be open-source and meet the different required functionalities and specifications. Many CAD and Slicing software programs exist, and some of them combine both functionalities. A list of selection criteria has been established in order to objectively select the most adequate software. Table  1  shows these selection criteria and a comparison between different software and slicers according to the criteria. The symbol "×" means that the software has this functionality (i.
e. , it meets this criterion). × × × OpenSCAD × × × IceSL × × × × × Three software packages are available offering the possibility to develop scripts. Blender is a powerful tool with interesting features; however, it is more adapted to work on meshes than on solids based on volume. OpenSCAD is more complete at the mathematical level, with libraries that avoid the implementation of complex mathematical structures such as the Lindenmeyer system  [31]. IceSL  [27] , which is a derivative of OpenSCAD, is a research tool that offers interesting features such as the direct generation of G-code from the model without the conversion to the STL format. This feature is key in the D printing of monoliths since it prevents degradation in the resolution of the geometry because of the conversion to the STL format.
Monolith Script Development In order to allow the creation of a customizable porous structure, i. e. , a monolith, a script has been developed in the Lua language  [36]  which should be loaded in the IceSL software. This script is open-source and available on https://github. com/RomainBedell/ Porous_medium_generator (accessed on 29 June 2021). The generated monolith model is a pseudo-random trabecular structure (open-cell) in which different cylindrical forms can be generated and customized. To facilitate the use of this script, a GUI has been developed that gives the user the control to configure the desired monolith. Figure  4  shows this interface while Table  2  explains the functionality of each parameter. Technologies 2021, 9, x FOR PEER REVIEW generate foam models respecting the constraints of 3D printing  [35].
Monolith Script Development In order to allow the creation of a customizable porous structure, i. e. , a script has been developed in the Lua language  [36]  which should be loaded software. This script is open-source and available on https://github. com dell/Porous_medium_generator (accessed on 29 June 2021). The generate model is a pseudo-random trabecular structure (open-cell) in which different forms can be generated and customized. To facilitate the use of this script, a G developed that gives the user the control to configure the desired monolit shows this interface while Table  2  explains the functionality of each paramete.
Analysis of Digital and 3D-Printed Monoliths IceSL allows slicing of the model by images in SVG (Scalable Vector Graphics) format. In order to evaluate the monoliths generated using the developed script, SVG images were used to compute monolith properties commonly used in soil science and geotechnical engineering. Figure  9  shows an example of an SVG image of a layer from Monolith 3. IceSL allows slicing of the model by images in SVG (Scalable Vect In order to evaluate the monoliths generated using the developed scrip used to compute monolith properties commonly used in soil science an neering. Figure  9  shows an example of an SVG image of a layer from M As a first step, the obtained data from the developed script we voxelized images. The size of a voxel is fixed at the layer thickness directions.
 Intergranular porosity is the ratio of volume of voids to total vol  Specific surface is the ratio of the total interface between solid an ume. The interface between solid and voids is computed using t algorithm with C6 connectivity (voxels are considered connected a face in common) in the MorphoLibJ package  [38]. As a first step, the obtained data from the developed script were transformed into voxelized images. The size of a voxel is fixed at the layer thickness (see Table  3 ) in all directions. This voxelization leads to staircase shapes of the interfaces between the solid and voids. Although this has little impact on the volumetric parameters, it can influence the surface calculations. All image analyses were performed using the open-source software ImageJ and its plugins XLib  [37]  and MorphoLibJ  [38].
3D Printing of the Monoliths In order to test the feasibility of the monolith models, Monolith 1 is 3D-printed using the Fused Filament Fabrication (FFF) technique in order to realize physical samples of the designed digital models. IceSL allows the direct slicing of the numerical model to the G-code and thus without passing by the STL file, this function is particularly interesting because it makes it possible to avoid the resolution loss and the limitations of the STL format. The RAISE3D E2  [42]  3D printer is used to fabricate Monolith 1 in PLA. Table  3  shows the main 3D printing parameters used to print it, while Figure  10  shows an image of the 3D-printed monolith. code and thus without passing by the STL file, this function is particularly intere cause it makes it possible to avoid the resolution loss and the limitations of the ST The RAISE3D E2  [42]  3D printer is used to fabricate Monolith 1 in PLA.
Granulometry According to the script description (see Section 2. 2. 4), the granulometry s mainly controlled by the "Grain Number" (GN) parameter and the "Infill" (In Figure  11 , the generated models have grain sizes ranging from 1 mm (coarse sa mm (fine gravel). Comparing monoliths 1 and 2, which have the same value of impact of the GN parameter can be observed. As expected, the higher the G smaller the grains are, ranging from a characteristic size of coarse sand for GN gravel for GN = 0. 2. The comparison between Monolith 3 and Monoliths 1, 2, and same GN value, shows the impact of the InF parameter on grain size. A decrea value of InF generates a decrease in grain size as well as a slight reduction in range. Finally, it should be pointed out that the root has no impact on the grain tribution since it was introduced after the monolith creation.
Granulometry According to the script description (see Section 2. 2. 4), the granulometry should be mainly controlled by the "Grain Number" (GN) parameter and the "Infill" (InF). From Figure  11 , the generated models have grain sizes ranging from 1 mm (coarse sand) to 12 mm (fine gravel). Comparing monoliths 1 and 2, which have the same value of InF, the impact of the GN parameter can be observed. As expected, the higher the GN is, the smaller the grains are, ranging from a characteristic size of coarse sand for GN = 0. 3 to gravel for GN = 0. 2. The comparison between Monolith 3 and Monoliths 1, 2, and 4, at the same GN value, shows the impact of the InF parameter on grain size. A decrease in the value of InF generates a decrease in grain size as well as a slight reduction in the size range.
Comparison with Printed Monolith In order to assess the accuracy of the 3D printing, a printed Monolith 1 was imaged using X-ray microtomography Phoenix Nanotom. Figure  13  shows the image obtained. Some local printing artifacts may be observed. Firstly, the partial filling (see Table  3 ) leads will not take part in fluid flow or particle transport within the sample. As a consequence, the following morphological comparison will be performed only considering the intergranular porosity. It should be noted that the non-connected intra-granular porosity is 2. 5%. A second artifact may be observed with the apparition of bridges between grains (Figure  13 ). Despite these observed artifacts, the comparison of morphological and physical properties between the original data and printed material remains satisfactory as shown in Table  5  and Figure  14.
Conclusions This study successfully utilized an open-source toolchain consisting of a Lua script in IceSL with a free GUI to enable researchers to create and configure digital soil models without resorting to meshing algorithms. The designed monoliths were fabricated with common and accessible 3D printers in the most used filament, PLA, with layer thicknesses of 0. 20, 0. 12, and 0. 08 mm. The images generated from the digital model slicing were analyzed using open-source ImageJ software to obtain information about internal geometrical shape, porosity, tortuosity, grain size distribution, and hydraulic conductivities. The results showed that the developed open-source script enabled all researchers to design reproducible numerical models that imitated soil structures with defined pore and grain sizes with the following observations: The granulometry curve and pore size distribution curve demonstrate good consistency between the digital model data and the 3D-printed data of Monolith 1, with a slight discrepancy only occurring for larger grains (representing a larger volume for the printed monolith) and pores (representing a smaller volume for the printed monolith).
Conclusions This study successfully utilized an open-source toolchain consisting of a Lua script in IceSL with a free GUI to enable researchers to create and configure digital soil models without resorting to meshing algorithms. The designed monoliths were fabricated with common and accessible 3D printers in the most used filament, PLA, with layer thicknesses of 0. 20, 0. 12, and 0. 08 mm. The images generated from the digital model slicing were analyzed using open-source ImageJ software to obtain information about internal geometrical shape, porosity, tortuosity, grain size distribution, and hydraulic conductivities. The results showed that the developed open-source script enabled all researchers to design reproducible numerical models that imitated soil structures with defined pore and grain sizes with the following observations:.
• The morphological and physical properties seem globally consistent. However, when the grains are mostly disconnected from each other, "bridges" must be created for 3D printing in order to obtain a "stable" structure. This will likely generate significant impacts on the porosity and probably on the pore size distribution. Samples that can be generated by the developed script would be expected to increase reproducibility as well as to be more accessible because of the open-source and low-cost methods involved. This work can be expanded in the future by using a smaller nozzle for FFF printing and applying the technique to other forms of 3D printing with higher resolution as biocompatible materials for the fabrication of the monoliths.
Introduction The introduction of Whole Slide Imaging (WSI) scanners enables the production of huge amounts of histological image data. A crucial step in the tissue preparation process is staining, which consists of dyeing the tissue slices with a specific stain in order to highlight internal structures when viewed under a microscope. Image datasets in digital pathology often consist of (nearly consecutive) tissue slides stained differently, each staining providing specific morphological information. As each staining highlights different components of the tissue, even consecutive sections largely representing almost identical anatomical structures can take on very different appearances, see Fig. 1. On the other hand, the staining process itself is prone to high variability due to inter-subject differences, the staining procedure in a specific lab, or scanner characteristics, leading to different appearances of the tissues coloured with the same stain  [25].
This means that models trained for a specific task on histological images of stain A exhibit a significant drop in performance when applied to histological images of stain B (for the same task)  [5, 17, 23]  or variability of stain A (e. g. images from other laboratories)  [45]. Typical solutions consider either fine tuning existing models or training a new model for each data variation, which requires the acquisition of additional labeled data. Nevertheless, medical image datasets are often characterised by their scarcity of annotations  [43]  and obtaining properly annotated images is time consuming and costly as expert knowledge is required for most complex annotations. Alternative approaches such as crowd sourcing are limited by the need of specific task design and intensive training  [16].
In this article we build upon our existing work  [23]  to introduce a new, general approach able to learn stain invariant representations  1  from only one annotated stain, without any other additional pre-or post-processing steps. We focus on the segmentation of glomeruli, a highly relevant functional unit of the kidney, and consider a dataset that contains WSIs stained with a commonly used staining in renal pathology-Periodic acid-Schiff reaction (PAS)-as the annotated data (source stain), in addition to WSIs that are stained with four different stainings-two histochemical (Sirius Red and Jones' basement membrane stain-Jones H&E) and two immunohistochemical stainings (CD34 highlighting blood vessel endothelium, CD68 for macrophages)-which are considered to be the unannotated datasets (target stains).
Once trained, the model can be directly applied to the test data. to present a discussion on the limitations of GAN-based imageto-image translations for stain transfer and to raise awareness of the possibility of misinterpretation during application in clinical practice, particularly, that visual quality assessments cannot be used as a stopping criteria during training stain transfer models. The remainder of this article is organised as follows: in Section 2, literature concerned with stain transfer and stain invariant approaches is reviewed. Section 3 gives a detailed description of the proposed method and dataset. Section 4 presents the experimental results with comparison to existing state-of-the-art methods. Finally, Section 5 analyses the approach in terms of stain invariance, quality of translations, and its limitations.
Data Tissue samples were collected from a cohort of 10 patients who underwent tumor nephrectomy due to renal carcinoma. The kidney tissue was selected as distant as possible from the tumors to display largely normal renal glomeruli, some samples included variable degrees of pathological changes such as full or partial replacement of the functional tissue by fibrotic changes (''scerosis") reflecting normal age-related changes or the renal consequences of general cardiovascular comorbidity (e. g. cardial arrhythmia, hypertension, arteriosclerosis). The paraffinembedded samples were cut into 3 lm thick sections and stained with either Jones' H&E basement membrane stain (Jones), PAS, Sirius Red or H&E, in addition to three immunohistochemistry markers (CD34, CD68 and CD3), using an automated staining instrument (Ventana Benchmark Ultra).
This is best illustrated with MDS1, in which a model trained on the original PAS data is applied to target data translated to PAS. It can be observed that in each target stain, the difference between CycleGAN and StarGAN translations is significant, although there appears to be no significant difference in the quality of the translations. The proposed UDA-GAN approaches show more stable performance while the best results are obtained using UDA-CGAN and Multi UDA-CGAN. Furthermore, UDA-CGAN reaches almost baseline performance in three out of five test stainings. Despite the fact that the model has seen data from PAS stain only 20% of the time during training, the model has baseline performance on this (source) domain. The model also approaches baseline performance in target stains Jones H&E and Sirius Red.
Feature distributions In order to visualise the distributions of each model's extracted features, Fig. 7  presents UMAP (Uniform Manifold Approximation and Projection) embeddings  [30]  of the penultimate convolutional layer's activations in the best performing model (over all stainings) for two hundred random glomeruli and two hundred random tissue patches of each staining using MDS1, MDS*1, UDA-CGAN, and UDA-*GAN (MDS2 and MDS*2 models are stain specific, therefore cannot be applied to all stainings). For MDS1 and MDS*1, the translations to the PAS stain is achieved using CycleGAN and Star-GAN respectively, and UDA-CGAN and UDA-*GAN are applied to original data without any modification. In order to quantitatively measure these distributions, silhouette scores  [38]  have been calculated between: each stain's glomeruli class and the union of the glomeruli samples from all other stains; PAS glomeruli and each target stain's glomeruli; each stain's glomeruli and negative samples.