dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, by using a 3D volume library, this local information can help capture the spatial layout of different MS lesions. Finally, neither  Weiss et al. (2013)  nor  Roy et al. 's (2014)  patch-based approaches for lesion segmentation use rotationally invariant features. As will be demonstrated in Section 3. 1. 3, this aspect is crucial in the context of MS lesion detection. We assessed our rotation-invariant multi-contrast non-local means segmentation (RMNMS) approach on 108 RRMS patients from a multi-site clinical study. Our method obtained a Dice similarity measure of 60. 1 ± 16. 4%, a sensitivity 75. 4 ± 15. 7% and a precision 55. 0 ± 20. 1% in cross-validation. Using the parameters established for our initial evaluation, we compared RMNMS to several different state-of-the-art techniques using the datasets from the MS lesion Grand Challenge  (MSGC, MICCAI, 2008 (Styner et al.
Datasets As mentioned by  García-Lorenzo et al. (2013) , simulated datasets, e. g. BrainWeb, enable a good proof of concept validation for image processing methods by providing ground truth images and lesion masks. However, BrainWeb images present multiple limitations: synthetic images are much easier to segment, only one phantom anatomy exists, and BrainWeb lacks contrasts such as FLAIR. Therefore, in this article we focus on two clinical datasets, an RRMS multi-center clinical dataset and the MICCAI (2008) MS Grand Challenge dataset  (Styner et al. , 2008).
Clinical MS dataset One clinical multi-center dataset of 108 RRMS patients [age = 42. 6 ± 10. 7, 72 females] was used to assess the proposed segmentation algorithm. The dataset contains T1W [TE = 9-11 ms, TR = 30-40 ms, flip angle = 30°, in-plane resolution = 0. 977 × 0. 977 mm 2 , slice thickness = 3 mm], T2W [TE = 66-100 ms, TR = 3550-6610 ms, flip angle = 90°, in-plane resolution = 0. 977 × 0. 977 mm 2 , slice thickness = 3 mm], PDW [TE = 10-18 ms, TR = 1867-3750 ms, flip angle = 90°, inplane resolution = 0. 977 × 0. 977 mm 2 , slice thickness = 3 mm] and FLAIR [TE = 59-94 ms, TR = 7977-9630 ms, TI = 1993-2500 ms, flip angle = 90°, in-plane resolution = 0. 977 × 0. 977 mm 2 , slice thickness = 3 mm] for all subjects. The MRI data were acquired at 32 sites on 1. 5 T scanners from different manufacturers: GE (n = 19), Philips (n = 3) and SIEMENS (n = 10).
We do not have access to demographic information for this dataset. This dataset also contains gold standard MS lesion segmentation labels that were first automatically segmented by a multi-spectral Bayesian classifier  (Francis, 2004)  with the T1W, T2W and PDW images and manually assessed and corrected by expert raters who underwent extensive training on similar MS patient MRI data. In a previous study  (Caramanos et al. , 2012) , seven raters with similar expertise, corrected the automatically generated lesion labels and were evaluated on a set of 10 MS patients with similar MRI protocols to those used in this study. Thanks to the initial automatic segmentation, this evaluation revealed an excellent inter-rater reliability relative to their trainer's reference segmentations (DSC = 93. 5 ± 1.
MS Grand Challenge (MSGC) dataset of MICCAI 2008 Our proposed RMNMS algorithm was further validated using the clinical data provided by the MS lesion segmentation challenge introduced at MICCAI (2008)  (Styner et al. , 2008). From the MS challenge website 1 , 20 training MR datasets with ground truth manual lesion segmentations and 23 testing cases were provided from the Boston Children's Hospital (CHB) and the University of North Carolina (UNC). While lesions masks for the 23 testing cases are not available for download, an automated system is available to evaluate the output of a given segmentation algorithm. We downloaded the co-registered T1W, T2W, FLAIR images for all 43 datasets as well as the ground truth lesion mask images for the 20 training datasets. All images were interpolated at 0.
Pre-processing and training library All the images from both MS datasets (clinical RRMS and MSGC) were processed using the same pipeline, which does: a) NLM image denoising  (Coupé et al. , 2008). b) Intensity non-uniformity correction (N 3 )  (Sled et al. , 1998). Linear intensity normalization of the image histogram to our in-house MS templates that were created with an unbiased template creation algorithm  (Fonov et al. , 2011)  from the 108 T1W images of the RRMS patients (Fig. 3 ). c) Linear registration of each T1W image to our MS template which is in the MNI152 template space  (Collins et al. , 1994). d) Rigid registration of the T2W and FLAIR to the T1W image, followed by resampling onto a 1 × 1 × 1 mm grid in the MNI space. Note that for the purpose of the validation describe here, we used the T1W as the reference image for registration, but other modalities (T2W, FLAIR…) could be chosen if a T1W image is not present or required.
Equation Units Dice similarity measure DSC 2 Â TP FP þ FN þ 2 Â TP % True positive rate or sensitivity TPR TP TP þ FN % LTPR LTP LTP þ LFN % Positive predictive value or precision PPV TP TP þ FP % LPPV LTP LTP þ LFP % Volume difference VolD Va-Vm j j Vm % False positive rate or fall-out FPR FP FP þ TP % Symmetric surface distance SurfD 1 na þ nm ð Þ ∑ na i¼1 d am a þ ∑ nm j¼1 d ma m ! mm used for the segmentation, was built using the output images from the pre-processing stages d, e and f. To double the size of the library and increase the spatial distribution of MS lesions, left-right-mirrored copies of each dataset were added to the library (Fig. 3 ).
General evaluation strategy On the clinical RRMS dataset, we performed a leave-one-out crossvalidation of the proposed RMNMS method. This leave-one-out crossvalidation is achieved by first removing the subject and its respective left-right-mirrored images from the training library, then the multimodal pre-selection of the N closest subjects are selected and finally the segmentation is performed. We first evaluated the performance of RMNMS with respect to the search area radius of the patches in the library and the number of preselected training subjects (as described in Section 2. 1. 2). We also assessed RMNMS using i) different contrast combinations (T1W + FLAIR, T2W + T1W + FLAIR and T2W + FLAIR contrasts), ii) the original L2-norm distance measure version of the NLM segmentation algorithm using T2W + FLAIR contrasts ("T2W + FLAIR NLM"), iii) without the left-right mirror addition to the training library, and iv) the LesionTOADS 2  approach proposed by  Shiee et al.
(2010). LesionTOADS is an iterative atlas based segmentation technique that uses a topological and a statistical atlas within the fuzzy C-means algorithm. As it was originally developed to segment healthy brain tissues  (Bazin and Pham, 2008) , the algorithm was adapted to use multi-contrast (T1W + FLAIR) and an extra lesion class within the WM class. LesionTOADS was chosen as it is publically available and obtained one the best results during the 2008 MSGC  (Styner et al. , 2008). Note, that the algorithm was used with its default parameters. Furthermore, we explored the effect of patients' total lesion-load and lesion-by-lesion detection measures on the RMNMS method. Two experiments were done using the MSGC dataset. First, our algorithm was validated on the training set using leave-one-out crossvalidation.
Second, our segmentation results on the testing MSGC dataset were submitted online For the online MSGC evaluation, we provided the lesion mask in native space after interpolation. The organizers normalized different metric results between 0 and 100, where 100 is a perfect score and 90 is the typical score of an independent rater as described by  Styner et al. (2008). The different metrics (volume difference "VolD", surface distance "SurfD", true positive rate "TPR" and false positive rate "FPR" (Table  1 )) were measured by comparing the automatic segmentation to the manual segmentation of two experts ("CHB" and "UNC").
05 ml and not surprisingly, it is easier to capture the presence of bigger lesions as demonstrated by the LTPR and LPPV (median results: LTPR = 100. 0 ± 16. 2% and LPPV = 100 ± 17. 6%). For the lesions smaller than 0. 05 ml, the results are not as good (median results: LTPR = 62. 5 ± 20. 9% and LPPV = 71. 7 ± 26. 2%). Table  2  Computational time results on the RRMS clinical dataset. The proposed method RMNMS, with T2W + FLAIR images, is compared to the original NLM segmentation approach with multi-contrast (MNLM) and a T1W + FLAIR and T2W + T1W + FLAIR version of RMNMS (T1W + FLAIR and T2W + T1W + FLAIR RMNMS). The best measures are shown in bold and the significant difference when comparing with T2W + FLAIR RMNMS is shown in red. The experiment was conducted with a patch radius of 1 voxel, a search area radius of 5 voxels, a pre-selection of 50 subjects for all the methods.
MSGC training dataset The 20 MSGC training subjects RMNMS segmentations were evaluated in a leave-one-out cross-validation using (20 -1) × 2 = 38 templates (including the mirrored images). We chose to use a bigger search area radius to compensate for the smaller number of training subject than was available in the RRMS validation. In order to capture the presence of lesions in a greater search area in the library the following parameters were used: patch size radius = 3 and search area radius = 7. It is interesting to note, that the DSC (43. 8 ± 16. 03) results of RMNMS on this dataset are significantly smaller than for the RRMS dataset (62. 3 ± 14. 6%). Similar comments can be made for the TPR of 43. 9 ± 19. 1% and the PPV of 48. 7 ± 17. 1%. Given the decreased accuracy of RMNMS with the MSGC dataset we decided to compare the two manual gold standard labels using the same metrics.
MSGC testing dataset The segmentation of the MSGC testing dataset was performed using the whole cohort of training subjects in the template library (20 × 2 = 40) with the same parameters as those used for the training experiment except for the pre-selection number that was set to 40. Our segmentation results were interpolated back to their original space and then uploaded to the MSGC website, where an objective independent automatic evaluation was performed. The MSGC provides a results archive, allowing us to compare the performance of our method with other groups. The results are summarized in Table  3. At the time of writing, RMNMS held the best result with an overall average summary score of 86. 1 (note that 90 corresponds to a segmentation accuracy reaching human expert inter-rater variability).
Discussion In this article, we proposed a new method to detect MS lesions using a training library containing T2W and FLAIR images along with manual T2W lesion masks. Our adaptation of the increasingly popular NLM segmentation method to MS lesions identification with a new multi-contrast and RI distance measure has proven to be highly competitive in our internal validation and in an independent comparison. On a large clinical dataset of 108 RRMS patient, the best compromise between sensitivity, specificity and computation time using leave-oneout cross-validation was obtained with a patch radius of 1 voxel, a search area radius of 5 voxels and a pre-selection of 50 subjects (median results: DSC = 60. 1 ± 16. 4%, TPR = 75. 4 ± 15. 7%, PPV = 55. 0 ± 20. 1%, VolD = 33. 5 ± 68. 9%, LTPR = 79. 8 ± 14.
6% and LPPV = 85. 7 ± 24. 2%). Given the large RRMS cohort size and variability (e. g. lesion load, age, sex, MRI protocols and scanner brand), these results rank among the best in the MS segmentation literature  (Lladó et al. , 2012). Furthermore, when compared to the state-of-the-art methods with the publicly available MSGC dataset used during the 2008 MICCAI challenge, the RMNMS yields highly competitive segmentation accuracy (best score, 86. 11) and produced segmentations that are comparable to the inter-rater variability. Our voxel-wise analysis showed promising result with respect to the ability to automatically define the volume and the boundary of the MS lesions. Moreover, our ability to segment MS lesions is relatively independent of the patient's lesion load and lesion location. We also investigated the ability of RMNMS to detect the presence of lesions as lesionwise measures are often more clinically relevant.
This difference can be easily explained by the characteristics of the structure to be segmented where spatial distribution, shape and size of MS lesions are not consistent and thus require a larger number of training subjects to capture this variability. Yet another advantage of the subject training pre-selection in the case of altered images is the selection of the "closest" subjects from the training library. Indeed, despite the presence of artifacts and abnormal intensity non-uniformity in the MSGC dataset  (García-Lorenzo et al. , 2008b) , RMNMS has proven to be highly accurate in part due to the preselection of the most representative training subjects. The comparison of MS lesion segmentation algorithms is a difficult task as described by  García-Lorenzo et al. (2013)  for multiple reasons: lack of publicly available datasets/methods, differing MRI contrasts, optimal parameters, and inter-rater segmentation variability.
Indeed, variation of MS lesion manually defined on the same subject by different experts has been reported to vary greatly by  Zijdenbos et al. (2002). The MSGC dataset  (Styner et al. , 2008 ) also shows significant interrater variability with VolD = 68% and SurfD = 4. 85 mm. More importantly, the MSGC training set has an inter-rater reliability of 25% (DSC). One assumes that the MSGC testing set is similar. Despite these criticisms, the organizers of the MSGC are to be congratulated as the MSGC dataset is the first publicly available MS lesion dataset and independent platform for segmentation algorithm validation and comparison. That being said, the MSGC results need to be interpreted carefully with certain limitations in mind. First, the low agreement between the raters should be used as a reference.
This can be done by mapping a 25% DSC to a 90% score to represent inter-rater variability when assessing methods. This poor inter-rater agreement may be due to the quality of the images and the presence of multiple artifacts as mentioned by  García-Lorenzo et al. (2008a). The high inter-rater variability for the gold standard MSGC labels results in an upper bound on the quality metrics, as it is not possible to simultaneously agree with multiple manual raters that do not agree. For these reasons it is not surprising that RMNMS obtained lower similarity measures on the MSGC than on the clinical RRMS dataset. Second, the MSGC provides pre-processed data (registration, interpolation…), which is not optimal for the different pre-processing steps specific to the different segmentation algorithms.
Finally, the online validation metrics are only voxel-wise measures, but the MS segmentation problem cannot be only seen as a voxel-wise or volume difference problem. MS lesion segmentation is also a detection problem especially in the context of clinical studies where a method should capture the presence of all individual lesions. This is not reflected in the global DSC, VolD and SurfD measurements. Despite these limitations, we compared our approach with state-ofart supervised and unsupervised methods (n N 45) by submitting our segmentation results of the 23 MS test subjects to the MSGC website  (Styner et al. , 2008). While our RMNMS approach attained the first position at the time of writing with a score of 86. 11, this result must be considered with the limitations described above. We feel that our evaluation with the multi-site clinical dataset is much more representative of quality and robustness of the RMNMS technique.
We also compared our approach on our RRMS dataset to the popular and publicly available LesionTOADS approach  (Shiee et al. , 2010). Compared to RMNMS, LesionTOADS is a topology preserving approach guided by probabilistic and topologic atlases. This approach was developed to segment T1W and FLAIR images and as any unsupervised approach it is less flexible to image variability that is not described by the underlying models. These differences could explain the better results obtained by RMNMS on both MS datasets. Future work will focus on improving segmentation results for smaller lesions, further decrease in the computational time with more advanced patch matching strategy  (Ta et al. , 2014) , investigate the performance and the pre-selection preferences with respect to scanner machine, site, gender and other clinical variables.
Acknowledgements The authors would like to thank  NeuroRx Research  for providing the expert manual segmentations,  Canadian Institutes Of Health Research  ( MOP-111169 & 84360 ), les  Fonds de Recherche Santé Québec  and the  MS Society of Canada  (BioMed PhD studentship, 691 and an  Operating grant  to DLC). This study has been carried out with financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the  Investments for the future Programme IdEx Bordeaux  ( ANR-10-IDEX-03-02 ),  Cluster of excellence CPU  and TRAIL ( HR-DTI ANR-10-LABX-57 ). This work has been supported also by the  Spanish  grant  TIN2013-43457-R  from the  Ministerio de Economia y competitividad.
Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today.
icos-infrastrucutre. eu), Published by Copernicus Publications. National Ecological Observatory Network (NEON) (www. neoninc. org) and AmeriFlux (http://ameriflux. lbl. gov/). On the global scale, the FLUXNET project that combines these regional and continental networks into an integrated global network started in 1998  (Baldocchi et al. , 2001). Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (more info on http: //www. fluxdata. org). As stated in  Baldocchi et al. (2001) , the three main scientific goals of the FLUXNET project are 1. to quantify the spatial differences in carbon dioxide and water vapour exchange rates that may be experienced within and across natural ecosystems and climatic gradients; 2. to quantify the temporal dynamics and variability of carbon, water and energy flux densities; and 3.
fluxdata. org/DataInfo/Dataset20Doc20Lib/CommonAnc. aspx. At the same address, coordinates (latitude and longitude) of each site are also available. The variables are classified into two main groups: 1. instantaneous: this group includes air temperature, vapour pressure deficit and wind speed, which are state variables where the instantaneous measurement is relevant as is; 2. averaged: includes the radiation and the precipitation where the relevant value is a flux measured over a time range. Timestamps in the data indicate the time of measurement in the case of "instantaneous" variables, and in the case of "averaged" variables, the end of the averaging period, which is, in general, 30 min (i. e. first data in the year are for 01 January; 00:30 for the instantaneous variables and for 01 January; 00:00-00:30 for the averaged variables).
De-biasing the ERA-I data We denote the original ERA-I meteorological data, E. In order to correct for the observed bias between E and F E , the slope (s) and the intercept (i) of the linear regression of F E against E are used. The de-biased ERA-I meteorological data is denoted E d and calculated as followed, for all fields except the precipitation field: E d = sE + i (4) For the Global Radiation and Wind Speed fields, when calculating the regression coefficients of the linear relationship, we force the intercept to 0 in order to avoid of having possibly negative radiations, or too flat regression slope for Wind Speed. For the Precipitation field, we do not expect that the timing of precipitations in the ERA-I dataset is accurate enough, for using the linear regression between F E and E as a way to de-bias E.
Reconstructing a diurnal cycle to the ERA-I data In order to use the de-biased meteorological fields of the ERA-I dataset to fill the gaps in the meteorological fields of the FLUXNET dataset, they need to be interpolated from the original 3-hourly time step to the half-hourly time step. For the instantaneous fields (all fields, except the Global Radiation, the Long Wave Radiation and the Precipitation fields), the 3-hourly data are simply linearly interpolated in order to reconstruct a diurnal cycle at a half-hourly resolution. The half-hourly de-biased field of ERA-I dataset is denoted E d F and is written as: Algorithm 3 for j = 1 : n F { l = ((j -1) r F -z)/r E E d F,j = E d int(l) ( mod (l, 1)) + E d int(l+1) (1 -mod (l, 1)) } The Global Radiation field is distributed as a function of the solar angle, based on a code initially developed by J.
De-biasing the ERA-I data We denote the original ERA-I meteorological data, E. In order to correct for the observed bias between E and F E , the For the Global Radiation and Wind Speed fields, when calculating the regression coefficients of the linear relationship, we force the intercept to 0 in order to avoid of having possibly negative radiations, or too flat regression slope for Wind Speed. For the Precipitation field, we do not expect that the timing of precipitations in the ERA-I dataset is accurate enough, for using the linear regression between F E and E as a way to de-bias E. Instead, we simply use the ratio of the sum of the elements of F E over the sum of the elements of E, denoted f. f is written as: f = nE j =1 F E,j nE j =1 E j (5) The de-biased Precipitation field of the ERA-I dataset, E d , is then defined as E d = f E.
De-biasing the ERA-I data We denote the original ERA-I meteorological data, E. In order to correct for the observed bias between E and F E , the slope (s) and the intercept (i) of the linear regression of F E against E are used. The de-biased ERA-I meteorological data d For the Global Radiation and Wind Speed fields, when calculating the regression coefficients of the linear relationship, we force the intercept to 0 in order to avoid of having possibly negative radiations, or too flat regression slope for Wind Speed. For the Precipitation field, we do not expect that the timing of precipitations in the ERA-I dataset is accurate enough, for using the linear regression between F E and E as a way to de-bias E. Instead, we simply use the ratio of the sum of the elements of F E over the sum of the elements of E, denoted f.
C. Morrill within the frame of the GSWP  (Dirmeyer, 2011)  and used in the ORCHIDEE model  (Krinner et al. , 2005)  for instance (http://dods. ipsl. jussieu. fr/orchidee/DOXYGEN/ webdoc/d1/db6/solar_8f90_source. html). The solar angle is a function of the longitude and latitude (lon, lat), the day of the year (doy) and the hour (hour in UTC + 0 time). The solar angle is denoted α(lon,lat,doy,hour) that we will restrict in the following to α(hour). The global radiation field is distributed as a function of the solar angle, based on a code initially developed by J. C. Morrill within the frame of the GSWP (Global Soil Wetness ProjectCE12;  Dirmeyer, 2011)  and used, for example, in the ORCHIDEE model  (Krinner et al. , 2005)  Please note the remarks at the end of the manuscript. Earth Syst. Sci.