Summary of brain lobes functional alterations: (a) inter-group and (b) intra-group.

<p>For this analysis, the brain is considered to be made up of six lobes as suggested by Salvador <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134944#pone.0134944.ref031" target="_blank">31</a>].</p

Classification accuracy with consistent neuroimaging marker identification method.

<p>For this experiment, 50% dataset is used for training and rest 50% for testing over 100 trials. It can be seen that the best accuracy is achieved with 450 connections.</p><p>Classification accuracy with consistent neuroimaging marker identification method.</p

Comparison of Zhang <i>et al</i>. [8] neuroimaging marker identification and clustering with our proposed methods.

<p>Abbreviations: DS–difference statistic neuroimaging marker identification method; AP–affinity propagation clustering method. The incremental comparison shows the promise of DS and AP clustering.</p

Relationship between the number of clusters found by AP clustering to the number of closest neighbors <i>n</i> in (4) used for computing the preference value <i>p</i>(<i>i</i>).

<p>(a) normal subject (80 in total), (b) epileptic patients (100 in total).</p

Additional file 1 of A model of the spatial tumour heterogeneity in colorectal adenocarcinoma tissue

File textFeat.xsl. Texture feature profiles for phenotypes found in the real data. Phenotypes are obtained based on the Haralick texture features shown in the table. For each feature the mean is shown in the first column and the standard deviation in the second column. (XLSX 17 kb

Brain region functional network: visualization of the correlation matrix [8] and community matrix obtained using (5).

<p>The difference between healthy and epileptic subjects is not prominent in the correlation matrix while it is prominent in community matrix (highlighted by boxes). This figure is suitable for visualization in color display.</p

The 30 most discriminant connections identified–the connections are sorted with respect to the corresponding absolute value in the connectivity difference matrix <i>D</i>.

<p>The positive sign of the <i>D</i> value represents increased connectivity in epilepsy patients while the negative sign represents decreased connectivity in epilepsy patients. Among these 30 connections, 17 are inter-hemispheric (i.e. between left and right hemi-spheres) which are highlighted in italic font. Out of these 17 connections, total 7 connections are between bilaterally homologous brain regions which are highlighted by * in the serial column. Abbreviations: L–left hemi-sphere, R–right hemi-sphere.</p><p>The 30 most discriminant connections identified–the connections are sorted with respect to the corresponding absolute value in the connectivity difference matrix <i>D</i>.</p

Additional file 1 of Robust normalization protocols for multiplexed fluorescence bioimage analysis

Appendix. Figure A-1: Column 1 to 4 represent four different cases: first two columns are from histologically normal tissue and the last two are from cancerous tissue of the same patient. Rows 1 to 4 represent pseudo-color images obtained after applying low rank normalization protocols as marked by the experts. Figure A-2: Within class KL-divergence for Patient 2. Within class KL-divergence for second patient after performing phenotyping using different normalization protocols. Figure A-3: Between class KL-divergence for Patient 2. Between class KL-divergence for second patient after performing phenotyping using different normalization protocols. (PDF 341 kb

A multi-organ nucleus segmentation challenge

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics