Rapid artefact removal and H&E-stained tissue segmentation.

We present an innovative method for rapidly segmenting haematoxylin and eosin (H&E)-stained tissue in whole-slide images (WSIs) that eliminates a wide range of undesirable artefacts such as pen marks and scanning artefacts. Our method involves taking a single-channel representation of a low-magnification RGB overview of the WSI in which the pixel values are bimodally distributed such that H&E-stained tissue is easily distinguished from both background and a wide variety of artefacts. We demonstrate our method on 30 WSIs prepared from a wide range of institutions and WSI digital scanners, each containing substantial artefacts, and compare it to segmentations provided by Otsu thresholding and Histolab tissue segmentation and pen filtering tools. We found that our method segmented the tissue and fully removed all artefacts in 29 out of 30 WSIs, whereas Otsu thresholding failed to remove any artefacts, and the Histolab pen filtering tools only partially removed the pen marks. The beauty of our approach lies in its simplicity: manipulating RGB colour space and using Otsu thresholding allows for the segmentation of H&E-stained tissue and the rapid removal of artefacts without the need for machine learning or parameter tuning.

Stain normalization gives greater generalizability than stain jittering in neural network training for the classification of coeliac disease in duodenal biopsy whole slide images.

Around 1% of the population of the UK and North America have a diagnosis of coeliac disease (CD), due to a damaging immune response to the small intestine. Assessing whether a patient has CD relies primarily on the examination of a duodenal biopsy, an unavoidably subjective process with poor inter-observer concordance. Wei et al. [11] developed a neural network-based method for diagnosing CD using a dataset of duodenal biopsy whole slide images (WSIs). As all training and validation data came from one source, there was no guarantee that their results would generalize to WSIs obtained from different scanners and laboratories. In this study, the effects of applying stain normalization and jittering to the training data were compared. We trained a deep neural network on 331 WSIs obtained with a Ventana scanner (WSIs; CD: n=190; normal: n=141) to classify presence of CD. In order to test the effects of stain processing when validating on WSIs scanned on varying scanners and from varying laboratories, the neural network was validated on 4 datasets: WSIs of slides scanned on a Ventana scanner (WSIs; CD: n=48; normal: n=35), WSIs of the same slides rescanned on a Hamamatsu scanner (WSIs; CD: n=48; normal: n=35), WSIs of the same slides rescanned on an Aperio scanner (WSIs; CD: n=48; normal: n=35), and WSIs of different slides scanned on an Aperio scanner (WSIs; CD: n=38; normal: n=37). Without stain processing, the F1 scores of the neural network were 0.947, 0.619, 0.746, and 0.727 when validating on the Ventana validation WSIs, Hamamatsu and Aperio rescans of the Ventana validation WSIs, and Aperio WSIs from a different source respectively. With stain normalization, the performance of the neural network improved significantly with respective F1 scores 0.982, 0.943, 0.903, and 0.847. Stain jittering resulted in a better performance than stain normalization when validating on data from the same source F1 score 1.000, but resulted in poorer performance than stain normalization when validating on WSIs from different scanners (F1 scores 0.939, 0.814, and 0.747). This study shows the importance of stain processing, in particular stain normalization, when training machine learning models on duodenal biopsy WSIs to ensure generalizability between different scanners and laboratories.

Multiple-instance-learning-based detection of coeliac disease in histological whole-slide images.

We present a multiple-instance-learning-based scheme for detecting coeliac disease, an autoimmune disorder affecting the intestine, in histological whole-slide images (WSIs) of duodenal biopsies. We train our model to detect 2 distinct classes, normal tissue and coeliac disease, on the patch-level, and in turn leverage slide-level classifications. Using 5-fold cross-validation in a training set of 1841 (1163 normal; 680 coeliac disease) WSIs, our model classifies slides as normal with accuracy (96.7±0.6)%, precision (98.0±1.7)%, and recall (96.8±2.5)%, and as coeliac disease with accuracy (96.7±0.5)%, precision (94.9±3.7)%, and recall (96.5±2.9)% where the error bars are the cross-validation standard deviation. We apply our model to 2 test sets: one containing 191 WSIs (126 normal; 65 coeliac) from the same sources as the training data, and another from a completely independent source, containing 34 WSIs (17 normal; 17 coeliac), obtained with a scanner model not represented in the training data. Using the same-source test data, our model classifies slides as normal with accuracy 96.5%, precision 98.4% and recall 96.1%, and positive for coeliac disease with accuracy 96.5%, precision 93.5%, and recall 97.3%. Using the different-source test data the model classifies slides as normal with accuracy 94.1% (32/34), precision 89.5%, and recall 100%, and as positive for coeliac disease with accuracy 94.1%, precision 100%, and recall 88.2%. We discuss generalising our approach to screen for a range of pathologies.

Reliability of 24-h void frequency as an index of hydration status when euhydrated and hypohydrated.

BACKGROUND/OBJECTIVES: Void frequency (VF) is significantly correlated to hydration status, but it is unknown whether VF is reliable when an individual is repeatedly euhydrated (EU) or hypohydrated (HY). The purpose of this study was to test the reliability of VF when individuals were EU or HY on multiple occasions. SUBJECTS/METHODS: Fourteen males (age 22±2 years, mass 79.1±12.8 kg) completed three EU trials achieved with 24-h ad libitum fluid intake, and 14 males (age 22±2 years, mass 78.6±10.4 kg) completed three HY trials achieved with 24-h fluid restriction. Twenty-four hour urine was collected and analyzed for specific gravity (USG) and VF. Subjects voided at a 'normal urgency' (rated a '2' on a 0-4 perceptual scale) throughout each 24-h period. RESULTS: Twenty-four hour USG was greater and VF was lower when HY (1.026±0.003 and 5±2, respectively) versus EU (1.014±0.003 and 7±2; both P<0.05). Intra-class correlations for VF between the three trials at each hydration status were deemed acceptable (0.863 and 0.849 for EU and HY, respectively). Within-subject coefficients of variation for VF were 15±9 and 21±14% for the EU and HY trials. CONCLUSIONS: VF is a reliable index of 24-h hydration status when healthy young males are EU or HY and voiding at a consistent 'urgency'.