dataesr/scientific-paragraphs-categorization huggingface dataset:
An optimized iDISCO+ protocol for tissue clearing and 3D analysis of oxytocin and vasopressin cell network in the developing mouse brain Here, we present an optimized iDISCO+ protocol combining tissue clearing and light sheet microscopy to map the postnatal development of oxytocin and vasopressin neurons in mouse hypothalamus. We describe tissue preparation, immunostaining, clearing, and imaging. We then detail how to process the 3D cell dataset to analyze cell network using a point-based recording procedure that accurately maps neurons in the Allen brain atlas. This protocol can be applied to any neuronal population, in different brain regions and at different developmental stages.
Imaging Timing: 3 h This section describes the procedure for imaging whole brain cell fluorescence with the light sheet microscope. Oxytocin and vasopressin cell bodies were imaged, the data was stored as tiff files and then 3D reconstruction and analysis were performed. 12. Imaging. a. Set the laser power (excitation 568 nm for oxytocin and 647 nm for vasopressin) around 20% for the 568 nm and 30% for the 647 nm, with a 150 ms time of exposure. b. For the adult brain, scanning hypothalamic regions in coronal plane (see Figure  8 ) (from the anterior commissure to the median eminence, see Figure  9A ); we used a step size of 2 mm and an optical zoom of 1. 6. Note: Optical zoom 1. 6 correspond to 1. 89 mm resolution in x and y. To obtain a cubic voxel, the step size must be set the same value than the microns size in x and y.
Timing: 3 days In this section, we report how to analyze oxytocin and vasopressin cell network. From light sheet microscope generated tiff files create a 3D file, then perform the quantification as described below. Excel files containing data such as cell size or fluorescence intensity can be obtained for all cells. 13. Conversion to 3D files: With the Imaris converter, you can export 2D tiff images (from the light sheet microscope) to a 3D ims file. 14. We performed automatic cell detection with Imaris software as well as through manual checking. For both global and region-by-region cell detection (as illustrated in Figure for ParaVentricular Nucleus) use the Imaris spot detection module. a. Set the cells diameter to 10 microns and start the detection for each channel, OXT and AVP, separately.
89 mm 3 ). 2. A hypothetical cell density was estimated by extracting SON and PVN volumes from the Allen mouse brain atlas with 25 mm resolution smoothed with a Gaussian filter of size s = 50 mm. 3. Considering N, the number of cell coordinates to align, N anchor points were generated from the estimated densities. 4. The translation T, rotation R, and scale S parameters enabling the alignment of the data in the atlas were estimated by applying a rigid point-based registration with the CPD (Coherent Point Drift) algorithm. 5. The estimated parameters (T, R and S) were used for alignment and visualization of other cell groups identified in the atlas during the same recording (Figure  11G ). 6. Since the PVN and SON definitions are of higher resolution in our recordings as compared to those in the atlas (5 mm thick virtual slices every two microns vs.
The procedure for identifying changes during PVN cells development was designed as follows: 7. Using the CPD method, we aligned the recorded adult PVN data set onto the spatial coordinates of previously identified adult PVN cells mapped in a reference space based on Allen brain atlas (Figures  12A  and 12B ). 8. The CPD method aligns all recorded cells of an experimental group to the coordinates of the nearest experimental group. Thus, 14-day data are aligned with adult data, 7-day data are aligned with 14-day data, and so forth (Figure  12C ). 9. With all the datasets aligned and scaled in the same space, it is then possible to use signal processing methods (e. g. , based on K-nearest neighbor methods) and statistical analyses to highlight experimental effects while controlling structure growth during development.
ACKNOWLEDGMENTS This research is funded by  Centre National de la Recherche Scientifique , le  Vinatier Hospital  and  Labex Cortex  ( ANR-11-LABEX-0042 ) to A. S. We thank the genotyping facility ( SFR Biosciences-ENS Lyon ) and the animal facility ( iXplora-CarMen Laboratory U1060 ) for allowing access to the animal facility.
Purpose: To assess the feasibility of a three-dimensional deep convolutional neural network (3D-CNN) for the general triage of whole-body FDG PET in daily clinical practice. Methods: An institutional clinical data warehouse working environment was devoted to this PET imaging purpose. Dedicated request procedures and data processing workflows were specifically developed within this infrastructure and applied retrospectively to a monocentric dataset as a proof of concept. A custom-made 3D-CNN was first trained and tested on an "unambiguous" well-balanced data sample, which included strictly normal and highly pathological scans. For the training phase, 90% of the data sample was used (learning set: 80%; validation set: 20%, 5-fold cross validation) and the remaining 10% constituted the test set.
Finally, the model was applied to a "real-life" test set which included any scans taken. Text mining of the PET reports systematically combined with visual rechecking by an experienced reader served as the standard-of-truth for PET labeling. Results: From 8125 scans, 4963 PETs had processable cross-matched medical reports. For the "unambiguous" dataset (1084 PETs), the 3D-CNN's overall results for sensitivity, specificity, positive and negative predictive values and likelihood ratios were 84%, 98%, 98%, 85%, 42. 0 and 0. 16, respectively (F1 score of 90%). When applied to the "real-life" dataset (4963 PETs), the sensitivity, NPV, LR+, LR-and F1 score substantially decreased (61%, 40%, 2. 97, 0. 49 and 73%, respectively), whereas the specificity and PPV remained high (79% and 90%). Conclusion: An AI-based triage of whole-body FDG PET is promising.
More specifically, these new image-processing methods use convolutional networks that have surpassed empirical methods by allowing the extraction of intrinsic features from images. Recently, several network architectures have emerged, such as ResNet, which have proven their superior performance of 2D classification tasks. PET-CT imaging consists of volumetric acquisition, which is why we decided to develop a 3D convolutional algorithm based on ResNet for our study. However, the validation of these new approaches requires a large amount of imaging data, which is often limited by access to large structured medical databases in practice  [11]. In this proof-of-concept study, an EDS environment was dedicated for the first time to PET imaging to assess the feasibility of a three-dimensional deep convolutional neural network (3D-CNN) for the general triage of whole-body FDG PET scans in daily clinical practice: the CLARITI project.
Dataset In this retrospective monocentric study, all the whole-body FDG PET/CT scans performed on adult patients willing to have their data reused for research between 2015 and 2019 at the Hôpitaux Universitaires Paris Saclay/AP-HP (CHU Bicêtre) were preprocessed. The project was approved by the institutional review board (authorization number IRB 00011591) of the scientific and ethical committee (CSE EDS n • 18-38, CLARITI). All scans were performed from vertex to mid-tight on the same PET/CT device (mCT flow, Siemens Healthineers, Erlangen, Germany) after an intravenous injection of 3. 5 MBq per kg and fulfilled the international procedure guidelines for FDG PET imaging in standard clinical routine  [3]. Image reconstruction was the same for all patients (3D OSEM with 2 iterations and 21 subsets with time-of-flight and point spread function modeling; matrix size: 400 × 400 voxels; voxel size = 2.
apache. org/ accessed on 22 April 2023)-a query language which allows Hive to process and analyze structured massive data in the Metastore-and PySpark (http://spark. apache. org/ accessed on 22 April 2023), a Python application programming interface for massive structured data processing. In this dedicated working space, the 3D PET data were processed and downsampled by the removal of the body-surrounding background, the conversion of voxel values from float32 to float16 and matrix size reduction Appl. Sci. 2023, 13, 5281 3 of 13 by a 3D kernel of 3 × 3 × 3 mean pooling and then normalized by z-scoring of the SUV values with standardization of the slice number (n = 549). All the 3D PET data were then labeled "normal" or "abnormal" through a two-step cross-validation procedure:.
CNN Architecture, Training and Testing Procedures The preprocessed 3D PET data were converted into tensors and fed as input, together with their corresponding binarized "label" predictions as output, to a 3D CNN. The CNN architecture is illustrated in Figure  1. PySpark (http://spark. apache. org/ accessed on 22 April 2023), a Python application programming interface for massive structured data processing. In this dedicated working space, the 3D PET data were processed and downsampled by the removal of the bodysurrounding background, the conversion of voxel values from float32 to float16 and matrix size reduction by a 3D kernel of 3 × 3 × 3 mean pooling and then normalized by zscoring of the SUV values with standardization of the slice number (n = 549). All the 3D PET data were then labeled "normal" or "abnormal" through a two-step cross-validation procedure:.
CNN Architecture, Training and Testing Procedures The preprocessed 3D PET data were converted into tensors and fed as input, together with their corresponding binarized "label" predictions as output, to a 3D CNN. The CNN architecture is illustrated in Figure  1. Briefly, we used a simplified convolutional neural network combining 5 blocks of a three-dimensional convolutional layer, a batch normalization layer and a 3D max-pooling layer, followed by a fully connected layer and a final 2-way activation layer (SoftMax function) for output binary classification purposes  [13]. Rectified linear units (ReLUs) were used as activation functions at all the hidden convolutional layers to add nonlinear complexity. To verify the ability of the CNN to recognize normal scans, an ideal dataset was created by selecting all the strictly normal and highly pathological PET data, excluding injection hot spots, drug-related digestive hypermetabolism, urinary catheters, renal transplants and sophisticated surgical anastomoses.
This ideal sample of strictly normal/highly pathological PET data (ratio approximately 50/50, respectively), named the "unambiguous" sample, was first used to train and test the CNN under optimal conditions. For this purpose, the "unambiguous" data sample was split into training and test Briefly, we used a simplified convolutional neural network combining 5 blocks of a three-dimensional convolutional layer, a batch normalization layer and a 3D max-pooling layer, followed by a fully connected layer and a final 2-way activation layer (SoftMax function) for output binary classification purposes  [13]. Rectified linear units (ReLUs) were used as activation functions at all the hidden convolutional layers to add nonlinear complexity. To verify the ability of the CNN to recognize normal scans, an ideal dataset was created by selecting all the strictly normal and highly pathological PET data, excluding injection hot spots, drug-related digestive hypermetabolism, urinary catheters, renal transplants and sophisticated surgical anastomoses.
This ideal sample of strictly normal/highly pathological PET data (ratio approximately 50/50, respectively), named the "unambiguous" sample, was first used to train and test the CNN under optimal conditions. For this purpose, the "unambiguous" data sample was split into training and test sets (ratio 90/10). Eighty percent of the training set served to educate the CNN about the binary cross-entropy loss function for a maximum of 100 epochs, with a batch size of 4 per iteration, using the Adam optimizer  [14]  with a learning rate of 1e -3. To prevent overfitting, the training was stopped once the model loss reached the minimum on the validation set. To check the model's stability, the trained CNN was assessed on the remaining 20% of the training set by using a stratified 5-fold cross-validation procedure  [15]  and tested on the test set (10% of the unambiguous dataset).
We ensured that no patients from the training set were included in the test set. In the second step, the CNN was trained by starting on the full dataset using a stratified 5-fold cross-validation procedure with the same training parameters, named the "real-life" data sample; it was then evaluated on the previously defined test set using the best fold. All the experiments were run inside the EDS dedicated environment on a GPU NVIDIA P40 (24 Go VRAM) and 48 CPU (188 Go of memory). All the CNN frameworks were built using the PyTorch backend.
Results A flowchart of the data selection process is provided in Figure  2. Appl. Sci. 2023, 13, x FOR PEER  REVIEW 4 o  sets (ratio 90/10). Eighty percent of the training set served to educate the CNN about binary cross-entropy loss function for a maximum of 100 epochs, with a batch size of 4 iteration, using the Adam optimizer  [14]  with a learning rate of 1. To prevent ove ting, the training was stopped once the model loss reached the minimum on the valida set. To check the model's stability, the trained CNN was assessed on the remaining 2 of the training set by using a stratified 5-fold cross-validation procedure  [15]  and tes on the test set (10% of the unambiguous dataset). We ensured that no patients from training set were included in the test set. In the second step, the CNN was trained by starting on the full dataset using a st ified 5-fold cross-validation procedure with the same training parameters, named "real-life" data sample; it was then evaluated on the previously defined test set using best fold.
Results A flowchart of the data selection process is provided in Figure  2. time of analysis (803 PETs with blank or incomplete reports dues to import or compatibility problems were discarded). After a visual evaluation of the 2D MIP projections of the overall available PET data, 252 exams were discarded due to poor quality (injected dose error, incomplete examination or patient movement artifacts that did not allow for the interpretation of the examination). Of the remaining 4964 PET data (3883 patients), 1084 PET data (986 patients) were included in the "unambiguous" dataset.
The heatmaps derived from the gradient weighted-class activation map method (Grad-CAM)  [16]  for one patient are provided in Figure  4. Fold 1 provided the best results, with a Sen of 94%, Spe of 100%, PPV and NPV of 100% and 94%, respectively, corresponding to an F1 score of 97%. The pretrained CNN then analyzed the independent remaining test set (108 PETs) and achieved a Se of 84% and Spe of 98%, a PPV and NPV of 98% and 85%, respectively, a F1 score of 90% and LR+ and LR-of 42 and 16%, respectively. The heatmaps derived from the gradient weightedclass activation map method (Grad-CAM)  [16]  for one patient are provided in Figure  4. The pretrained CNN then analyzed the remaining database composed of the "ambiguous" PETs (3911 PETs) and achieved a Se of 26% and Spe of 86%, a PPV and NPV of 97.
Our results from the "unambiguous" dataset are close to those obtained by  Kawauchi et al. for the classification of clinical FDG PET MIP into benign/malignant or equivocal categories  [19]. In their study, a ResNet-based 2D-CNN architecture reached 87. 5-99% accuracy depending on the category. A low representation of non-oncological patients and equivocal scans, together with unknown characteristics of the training and test sets, should position their study as done under ideal conditions. Indeed, when applied to the remaining test set composed of ambiguous PET studies, our model performance dropped dramatically, reaching an NPV of only 5%. Recently, Sibille L. and coworkers  [20]  showed that their 2D neural network was highly efficient at identifying and delineating suspicious and nonsuspicious whole-body PET/CT uptakes in a monocentric cohort of 629 lymphoma and lung cancer patients.
From this realistic perspective, and despite the technical challenges of the intrinsic variability within the PET data, we believe in the relevance of the automated identification of nonambiguous normal scans for general high-throughput triage in clinical practice. Our study has several limitations. First, this was a monocentric and single vendor PET-CT, retrospective study. However, approximately 5000 structured PET data were used in this proof-of-concept study, increasing the majority of already published cohorts in this field by a factor of five to 20. Second, the "real-life" test set was highly unbalanced in favor of abnormal scans, contrary to the unambiguous dataset. This could explain the poor performance we observed when we initially tested the CNN pretrained with unambiguous data on the remaining equivocal data (NPV of 5%).
We thus retrained the CNN from scratch, with the same training parameters and architecture, using the whole dataset ("real life"). This trained CNN showed an overall weaker performance with a PPV and NPV of 91% and 26%, but was comparable to its performance on the "unambiguous" data. These results may indicate better feature learning when realistic PET data ambiguity is integrated from the training phase. In this context, the definition of FDG PET normality is crucial but not absolute: without the particular context, the inherent heterogeneity in patients' whole-body FDG uptake patterns is high, regardless of the monocentric and multicenter designs. Although the performance of the model is promising, its routine clinical use is not yet possible. There are several avenues of improvement to explore.
Contributions: Design, acquisition analysis, revising for intellectual content, final approval and agreement to be accountable for all aspects of this work (accuracy and integrity of any part of the work): A. B. , H. D. , A. M. , C. P. , C. H. -K. , S. B. , C. D. , P. M. , L. G. , M. F. , E. D. and F. L. B. PET data processing and CNN building: A. B. Data warehouse logistic supervision and deployment for this PET study: F. L. B. , A. M. , C. P. , C. H. -K. , S. B. , C. D. , P. M. , L. G. and M. F. All authors have read and agreed to the published version of the manuscript. Funding: This work was partially supported by the French government through the 3IA Côte d'Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.
Acknowledgments: The authors would like to thank  Cyrina SAUSSOL  and  Yannick JACOB  for their technical help throughout the project, as well as the Department I&D-PACTE in charge of the implementation of EDS. The authors would also like to thank the  AP-HP  clinical research and innovation department, in particular the  AP-HP, Université Paris Saclay research unit  for their support in completing this project.
Author Contributions: Design, acquisition analysis, revising for intellectual content, final approval and agreement to be accountable for all aspects of this work (accuracy and integrity of any part of the work): A. B. , H. D. , A. M. , C. P. , C. H. -K. , S. B. , C. D. , P. M. , L. G. , M. F. , E. D. and F. L. B. PET data processing and CNN building: A. B. Data warehouse logistic supervision and deployment for this PET study: F. L. B. , A. M. , C. P. , C. H. -K. , S. B. , C. D. , P. M. , L. G. and M. F. All authors have read and agreed to the published version of the manuscript. Funding: This work was partially supported by the French government through the 3IA Côte d'Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.
Image analysis Timing: 30-60 min This section describes how to analyze functional imaging data. 19. Motion Correction: Prior to extraction of fluorescence time courses of individual neurons, unwanted subject motion in the X and Y planes may be eliminated through the use of ImageJ/FIJI (https://imagej. net/software/fiji/) and the ''moco'' plugin (https://github. com/ NTCColumbia/moco). 20. Several freely available programs exist for post-processing and analysis of fluorescence time courses such as those acquired with genetically encoded calcium indicators (GECIs). Post-processing analyses may include number of cells, spike rate, peak DF/F, correlation of activity, etc. a. The MATLAB toolbox published by Romano and colleagues (https://github. com/ zebrain-lab/Toolbox-Romano-et-al) provides a pipeline and a step-by-step explanation for automated motion correction, segmentation of individual neurons, extraction of fluorescence time courses, spike estimation, and other post-processing analyses.
Background: White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer's disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research. Methods: We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum.
for a recent review  (6) ]. The algorithms applied by these tools include Markov random fields  (7) , support vector machines  (8)  or K-nearest neighbors  (9). Recently, a new class of algorithms has emerged based on deep neural networks, yielding topranking results in the recent Medical Image Computing and Computer Assisted Intervention (MICCAI) challenge on WMH segmentation  (10). Nevertheless, the choice of a relevant method for WMH detection is difficult with a variety of available tools and evaluation criteria. Moreover, most of the original studies tested the methods on images acquired from a single MRI scanner, such that robustness and reliability still need to be proven for multicenter data. Regular challenges, as provided by the MICCAI society, constitute an excellent competition for state-of-the-art segmentation techniques on well-documented datasets.
For example, the last MICCAI challenge on WMH segmentation  (10)  used a manually segmented dataset in a multicenter context. Unfortunately, the WMH segmentation tools most frequently used in research studies are often neglected in the MICCAI challenge, thus limiting our understanding of the evolution and performance of new tools compared to already published ones. Recent studies compared multiple automatic WMH segmentation tools using multicenter databases  (6, 11, 12). With a rigorous methodology, Heinen et al. (11)  showed best performances for k-nearest neighbor classification with tissue type priors (kNN-TMP)  (13)  and, to a lesser extent, for lesion growth algorithm (LGA)  (7)  and lesion prediction algorithm (LPA)  (14) , both included in lesion segmentation toolbox (LST). Vanderbecq et al.