Update paper.bib and paper.md

paper.bib

@@ -24,7 +24,6 @@ @article{bankhead2017
   doi = {10.1038/s41598-017-17204-5},
   url = {https://www.nature.com/articles/s41598-017-17204-5},
   urldate = {2023-09-06},
-  abstract = {Abstract             QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.},
   langid = {english}
 }
 @article{yates2019,
@@ -54,7 +53,6 @@ @article{puchades2019
   pages = {e0216796},
   issn = {1932-6203},
   doi = {10.1371/journal.pone.0216796},
-  abstract = {Modern high throughput brain wide profiling techniques for cells and their morphology, connectivity, and other properties, make the use of reference atlases with 3D coordinate frameworks essential. However, anatomical location of observations made in microscopic sectional images from rodent brains is typically determined by comparison with 2D anatomical reference atlases. A major challenge in this regard is that microscopic sections often are cut with orientations deviating from the standard planes used in the reference atlases, resulting in inaccuracies and a need for tedious correction steps. Overall, efficient tools for registration of large series of section images to reference atlases are currently not widely available. Here we present QuickNII, a stand-alone software tool for semi-automated affine spatial registration of sectional image data to a 3D reference atlas coordinate framework. A key feature in the tool is the capability to generate user defined cut planes through the reference atlas, matching the orientation of the cut plane of the sectional image data. The reference atlas is transformed to match anatomical landmarks in the corresponding experimental images. In this way, the spatial relationship between experimental image and atlas is defined, without introducing distortions in the original experimental images. Following anchoring of a limited number of sections containing key landmarks, transformations are propagated across the entire series of sectional images to reduce the amount of manual steps required. By having coordinates assigned to the experimental images, further analysis of the distribution of features extracted from the images is greatly facilitated.},
   langid = {english}
 }
 @article{carey2023,
@@ -69,7 +67,6 @@ @article{carey2023
   pages = {5884},
   issn = {2041-1723},
   doi = {10.1038/s41467-023-41645-4},
-  abstract = {Abstract             Registration of data to a common frame of reference is an essential step in the analysis and integration of diverse neuroscientific data. To this end, volumetric brain atlases enable histological datasets to be spatially registered and analyzed, yet accurate registration remains expertise-dependent and slow. In order to address this limitation, we have trained a neural network, DeepSlice, to register mouse brain histological images to the Allen Brain Common Coordinate Framework, retaining registration accuracy while improving speed by {$>$}1000 fold.},
   langid = {english}
 }
 @article{berg2019,
@@ -98,7 +95,6 @@ @article{tyson2021
   pages = {e1009074},
   issn = {1553-7358},
   doi = {10.1371/journal.pcbi.1009074},
-  abstract = {Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.},
   langid = {english}
 }
 @article{tyson2022,
@@ -112,7 +108,6 @@ @article{tyson2022
   pages = {867},
   issn = {2045-2322},
   doi = {10.1038/s41598-021-04676-9},
-  abstract = {Abstract             High-resolution whole-brain microscopy provides a means for post hoc determination of the location of implanted devices and labelled cell populations that are necessary to interpret in vivo experiments designed to understand brain function. Here we have developed two plugins (brainreg and brainreg-segment) for the Python-based image viewer napari, to accurately map any object in a common coordinate space. We analysed the position of dye-labelled electrode tracks and two-photon imaged cell populations expressing fluorescent proteins. The precise location of probes and cells were physiologically interrogated and revealed accurate segmentation with near-cellular resolution.},
   langid = {english}
 }
 @article{schindelin2012,

paper.md

@@ -8,9 +8,8 @@ tags:
   - processing
   - neuroscience
 authors:
-  - name:
-      given-names: Guillaume
-      surname: Le Goc
+    - firstname: Guillaume
+    surname: Le Goc
     orcid: 0000-0002-6946-1142
     affiliation: 1
   - name: Julien Bouvier