One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

Automatic lesion segmentation; Convolutional neural networks; Multiple sclerosisSegmentació automàtica de les lesions ; Xarxes neuronals convolucionals; Esclerosi múltipleSegmentación automática de las lesiones ; Redes neuronales convolucionales; Esclerosis múltipleIn recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. In both datasets, our results showed the effectiveness of the proposed model in adapting previously acquired knowledge to new image domains, even when a reduced number of training samples was available in the target dataset. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single case showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling

J-PLUS: Photometric Re-calibration with the Stellar Color Regression Method and an Improved Gaia XP Synthetic Photometry Method

We employ the corrected Gaia Early Data Release 3 (EDR3) photometric data and spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR7 to assemble a sample of approximately 0.25 million FGK dwarf photometric standard stars for the 12 J-PLUS filters using the Stellar Color Regression (SCR) method. We then independently validated the J-PLUS DR3 photometry, and uncovered significant systematic errors: up to 15 mmag in the results of Stellar Locus (SL) method, and up to 10 mmag mainly caused by magnitude-, color-, and extinction-dependent errors of the Gaia XP spectra with the Gaia BP/RP (XP) Synthetic Photometry (XPSP) method. We have also further developed the XPSP method using the corrected Gaia XP spectra by Huang et al. (2023) and applied it to the J-PLUS DR3 photometry. This resulted in an agreement of 1-5 mmag with the SCR method, and a two-fold improvement in the J-PLUS zero-point precision. Finally, the zero-point calibration for around 91% of the tiles within the LAMOST observation footprint is determined through the SCR method, with the remaining approximately 9% of tiles outside this footprint relying on the improved XPSP method. The re-calibrated J-PLUS DR3 photometric data establishes a solid data foundation for conducting research that depends on high-precision photometric calibration.Comment: 21 papes; 20 figures, submitted, see main results in Figures 5 and 1

The miniJPAS survey : The role of group environment in quenching star formation

Peer reviewe

J-PLUS:Photometric calibration of large area multi-filter surveys with stellar and white dwarf loci

We present the photometric calibration of the twelve optical passbands observed by the Javalambre Photometric Local Universe Survey (J-PLUS). The proposed calibration method has four steps: (i) definition of a high-quality set of calibration stars using Gaia information and available 3D dust maps; (ii) anchoring of the J-PLUS gri passbands to the Pan-STARRS photometric solution, accounting for the variation of the calibration with the position of the sources on the CCD; (iii) homogenization of the photometry in the other nine J-PLUS filters using the dust de-reddened instrumental stellar locus in (X - r) versus (g - i) colours, where X is the filter to calibrate. The zero point variation along the CCD in these filters was estimated with the distance to the stellar locus. Finally, (iv) the absolute colour calibration was obtained with the white dwarf locus. We performed a joint Bayesian modelling of eleven J-PLUS colour-colour diagrams using the theoretical white dwarf locus as reference. This provides the needed offsets to transform instrumental magnitudes to calibrated magnitudes outside the atmosphere. The uncertainty of the J-PLUS photometric calibration, estimated from duplicated objects observed in adjacent pointings and accounting for the absolute colour and flux calibration errors, are ~19 mmag in u, J0378 and J0395, ~11 mmag in J0410 and J0430, and ~8 mmag in g, J0515, r, J0660, i, J0861, and z. We present an optimized calibration method for the large area multi-filter J-PLUS project, reaching 1-2% accuracy within an area of 1 022 square degrees without the need for long observing calibration campaigns or constant atmospheric monitoring. The proposed method will be adapted for the photometric calibration of J-PAS, that will observe several thousand square degrees with 56 narrow optical filters

Gray matter imaging in multiple sclerosis: what have we learned?

At the early onset of the 20th century, several studies already reported that the gray matter was implicated in the histopathology of multiple sclerosis (MS). However, as white matter pathology long received predominant attention in this disease, and histological staining techniques for detecting myelin in the gray matter were suboptimal, it was not until the beginning of the 21st century that the true extent and importance of gray matter pathology in MS was finally recognized. Gray matter damage was shown to be frequent and extensive, and more pronounced in the progressive disease phases. Several studies subsequently demonstrated that the histopathology of gray matter lesions differs from that of white matter lesions. Unfortunately, imaging of pathology in gray matter structures proved to be difficult, especially when using conventional magnetic resonance imaging (MRI) techniques. However, with the recent introduction of several more advanced MRI techniques, the detection of cortical and subcortical damage in MS has considerably improved. This has important consequences for studying the clinical correlates of gray matter damage. In this review, we provide an overview of what has been learned about imaging of gray matter damage in MS, and offer a brief perspective with regards to future developments in this field

J-PLUS: The Javalambre Photometric Local Universe Survey

The Javalambre Photometric Local Universe Survey (J-PLUS) is an ongoing 12-band photometric optical survey, observing thousands of square degrees of the Northern Hemisphere from the dedicated JAST/T80 telescope at the Observatorio Astrofisico de Javalambre (OAJ). The T80Cam is a camera with a field of view of 2 deg(2) mounted on a telescope with a diameter of 83 cm, and is equipped with a unique system of filters spanning the entire optical range (3500-10 000 angstrom). This filter system is a combination of broad-, medium-, and narrow-band filters, optimally designed to extract the rest-frame spectral features (the 3700-4000 angstrom Balmer break region, H delta, Ca H+K, the G band, and the Mg b and Ca triplets) that are key to characterizing stellar types and delivering a low-resolution photospectrum for each pixel of the observed sky. With a typical depth of AB similar to 21.25 mag per band, this filter set thus allows for an unbiased and accurate characterization of the stellar population in our Galaxy, it provides an unprecedented 2D photospectral information for all resolved galaxies in the local Universe, as well as accurate photo-z estimates (at the delta z/(1 + z) similar to 0.005-0.03 precision level) for moderately bright (up to r similar to 20 mag) extragalactic sources. While some narrow-band filters are designed for the study of particular emission features ([O II]/lambda 3727, H alpha/lambda 6563) up to z < 0.017, they also provide well-defined windows for the analysis of other emission lines at higher redshifts. As a result, J-PLUS has the potential to contribute to a wide range of fields in Astrophysics, both in the nearby Universe (Milky Way structure, globular clusters, 2D IFU-like studies, stellar populations of nearby and moderate-redshift galaxies, clusters of galaxies) and at high redshifts (emission-line galaxies at z approximate to 0.77, 2.2, and 4.4, quasi-stellar objects, etc.). With this paper, we release the first similar to 1000 deg(2) of J-PLUS data, containing about 4.3 million stars and 3.0 million galaxies at r < 21 mag. With a goal of 8500 deg(2) for the total J-PLUS footprint, these numbers are expected to rise to about 35 million stars and 24 million galaxies by the end of the survey

Ocrelizumab versus Interferon Beta-1a in Relapsing Multiple Sclerosis

Supported by F. Hoffmann–La Roche

Evidence for grey matter MTR abnormality in minimally disabled patients with early relapsing-remitting multiple sclerosis

Objectives: To establish whether magnetisation transfer ratio (MTR) histograms are sensitive to change in normal appearing grey matter (NAGM) in early relapsing-remitting multiple sclerosis (RRMS) in the absence of significant disability; and to assess whether grey or white matter MTR measures are associated with clinical measures of impairment in early RRMS Methods: 38 patients were studied (mean disease duration 1.9 years (range 0.5 to 3.7); median expanded disability status scale (EDSS) 1.5 (0 to 3)), along with 35 healthy controls. MTR was determined from proton density weighted images with and without MT presaturation. SPM99 was used to generate normal appearing white matter (NAWM) and NAGM segments of the MTR map, and partial voxels were minimised with a 10 pu threshold and voxel erosions. Mean MTR was calculated from the tissue segments. Atrophy measures were determined using a 3D fast spoiled gradient recall sequence from 37 patients and 17 controls. Results: Mean NAGM and NAWM MTR were both reduced in early RRMS (NAGM MTR: 31.9 pu in patients v 32.2 pu in controls; p<0.001; NAWM MTR: 37.9 v 38.3 pu, p = 0.001). Brain parenchymal fraction (BPF) correlated with NAGM MTR, but when BPF was included as a covariate NAGM MTR was still lower in the patients (p = 0.009). EDSS correlated with NAGM MTR (r = 0.446 p = 0.005). Conclusions: In early RRMS, grey matter MTR abnormality is apparent. The correlation with mild clinical impairment (in this essentially non-disabled cohort) suggests that NAGM MTR could be a clinically relevant surrogate marker in therapeutic trials