Images of differences for a true split and a typical random permutation split.

<p>The same methodology as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064464#pone-0064464-g002" target="_blank">Figure 2</a> is applied. (A) Random permutation split number 413 without filters. This permutation has a total number of 3518 dots after the median filter. Since this value is almost the median number of dots (3517) over all 500 random permutation splits, permutation 413 was selected as a typical representative. (B) True split without filters. (C) The image of random permutation split 413 after median filter. (D) The image of the true split after application of the median filter. (E) Number of differences after median filter for random permutation split 413, as measured by the t-tests, for all regions as a sum over time. (F) Number of differences after median filter for the true split, as measured by the t-tests, for all regions as a sum over time.</p

Illustration of the overall TCRpMHC complex.

<p><b>Blue: MHC alpha chain.</b> Red: Beta-2 microglobulin. Green: Presented Peptide in the MHC binding groove. Orange: TCR alpha chain. Yellow: TCR beta chain.</p

Comparison of groupM and groupL over a simulation time of 50 ns at threshold 10-⁵ M.

<p>For means of readability the curves of A and B were smoothed using a moving average. The mean value is indicated by a thick line while the mean value +/− the standard error of mean is indicated by thin lines. (A) RMSD TCR alpha chain. (B) RMSD TCR beta chain. (C-H) RMSF of the CDR regions.</p

TCR regions of interest according to Kjer-Nielsen et al. and VMD.

<p>TCR regions of interest according to Kjer-Nielsen et al. and VMD.</p

Effect of the median-method.

<p>(A) The identical data as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064464#pone-0064464-g002" target="_blank">Figure 2A</a> is illustrated. Again a black dot at time step x and region y indicates that a t-test{[(all “groupM” RMSD values), (all “groupL” RMSD values)] | x, y, α} yielded a difference. This approach is termed the direct-method. (B) The identical data as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064464#pone-0064464-g002" target="_blank">Figure 2A</a> is illustrated, however, processed with the median-method.</p

Illustration of the most frequently highlighted regions.

<p>White and solid: TCR. White and transparent: MHC. Red: peptide. Green: top 5 most frequently highlighted regions per threshold (i.e. 20 regions where several of them are identical). If the whole TCR beta chain was within the top regions it was not coloured in green for reasons of visibility. (A) Direct-method. (B) Median-method. (C) Square-method.</p

Distribution of s in 500 random permutation splits.

<p>The 95% percentile and the tested map (value of s for the true split) are indicated for the four different thresholds per method. (A) Direct-method. (B) Median-method. (C) Square-method.</p

Investigating the impact of the bit depth of fluorescence-stained images on the performance of deep learning-based nuclei instance segmentation

Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository

Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification

Background and objective: Skin cancer is among the most common cancer types in the white population and consequently computer aided methods for skin lesion classification based on dermoscopic images are of great interest. A promising approach for this uses transfer learning to adapt pre-trained convolutional neural networks (CNNs) for skin lesion diagnosis. Since pre-training commonly occurs with natural images of a fixed image resolution and these training images are usually significantly smaller than dermoscopic images, downsampling or cropping of skin lesion images is required. This however may result in a loss of useful medical information, while the ideal resizing or cropping factor of dermoscopic images for the fine-tuning process remains unknown. Methods: We investigate the effect of image size for skin lesion classification based on pre-trained CNNs and transfer learning. Dermoscopic images from the International Skin Imaging Collaboration (ISIC) skin lesion classification challenge datasets are either resized to or cropped at six different sizes ranging from 224 × 224 to 450 × 450. The resulting classification performance of three well established CNNs, namely EfficientNetB0, EfficientNetB1 and SeReNeXt-50 is explored. We also propose and evaluate a multi-scale multi-CNN (MSM-CNN) fusion approach based on a three-level ensemble strategy that utilises the three network architectures trained on cropped dermoscopic images of various scales. Results: Our results show that image cropping is a better strategy compared to image resizing delivering superior classification performance at all explored image scales. Moreover, fusing the results of all three fine-tuned networks using cropped images at all six scales in the proposed MSM-CNN approach boosts the classification performance compared to a single network or a single image scale. On the ISIC 2018 skin lesion classification challenge test set, our MSM-CNN algorithm yields a balanced multi-class accuracy of 86.2% making it the currently second ranked algorithm on the live leaderboard. Conclusions: We confirm that the image size has an effect on skin lesion classification performance when employing transfer learning of CNNs. We also show that image cropping results in better performance compared to image resizing. Finally, a straightforward ensembling approach that fuses the results from images cropped at six scales and three fine-tuned CNNs is shown to lead to the best classification performance

A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images

Even in the era of precision medicine, with various molecular tests based on omics technologies available to improve the diagnosis process, microscopic analysis of images derived from stained tissue sections remains crucial for diagnostic and treatment decisions. Among other cellular features, both nuclei number and shape provide essential diagnostic information. With the advent of digital pathology and emerging computerized methods to analyze the digitized images, nuclei detection, their instance segmentation and classification can be performed automatically. These computerized methods support human experts and allow for faster and more objective image analysis. While methods ranging from conventional image processing techniques to machine learning-based algorithms have been proposed, supervised convolutional neural network (CNN)-based techniques have delivered the best results. In this paper, we propose a CNN-based dual decoder U-Net-based model to perform nuclei instance segmentation in hematoxylin and eosin (H&E)-stained histological images. While the encoder path of the model is developed to perform standard feature extraction, the two decoder heads are designed to predict the foreground and distance maps of all nuclei. The outputs of the two decoder branches are then merged through a watershed algorithm, followed by post-processing refinements to generate the final instance segmentation results. Moreover, to additionally perform nuclei classification, we develop an independent U-Net-based model to classify the nuclei predicted by the dual decoder model. When applied to three publicly available datasets, our method achieves excellent segmentation performance, leading to average panoptic quality values of 50.8%, 51.3%, and 62.1% for the CryoNuSeg, NuInsSeg, and MoNuSAC datasets, respectively. Moreover, our model is the top-ranked method in the MoNuSAC post-challenge leaderboard