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Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable success in segmenting lungs from CXR images with normal or mildly abnormal findings, their performance declines when faced with complex structures, such as pulmonary opacifications. In this study, we propose AMRU++, an attention-based multi-residual UNet++ network designed for robust and accurate lung segmentation in CXR images with both normal and severe abnormalities. The model incorporates attention modules to capture relevant spatial information and multi-residual blocks to extract rich contextual and discriminative features of lung regions. To further enhance segmentation performance, we introduce a data augmentation technique that simulates the features and characteristics of CXR pathologies, addressing the issue of limited annotated data. Extensive experiments on public and private datasets comprising 350 cases of pneumoconiosis, COVID-19, and tuberculosis validate the effectiveness of our proposed framework and data augmentation technique., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Although F-actin has a large number of binding partners and regulators, the number of phenotypic states available to the actin cytoskeleton is unknown. Here, we quantified 74 features defining filamentous actin (F-actin) and cellular morphology in >25 million cells after treatment with a library of 114,400 structurally diverse compounds. After reducing the dimensionality of these data, only ∼25 recurrent F-actin phenotypes emerged, each defined by distinct quantitative features that could be machine learned. We identified 2,003 unknown compounds as inducers of actin-related phenotypes, including two that directly bind the focal adhesion protein, talin. Moreover, we observed that compounds with distinct molecular mechanisms could induce equivalent phenotypes and that initially divergent cellular responses could converge over time. These findings suggest a conceptual parallel between the actin cytoskeleton and gene regulatory networks, where the theoretical plasticity of interactions is nearly infinite, yet phenotypes in vivo are constrained into a limited subset of practicable configurations., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Microbial colony growth can serve as a useful readout in assays for studying complex genetic interactions or the effects of chemical compounds. Although computational tools for acquiring quantitative measurements of microbial colonies have been developed, their utility can be compromised by inflexible input image requirements, non-trivial installation procedures, or complicated operation. Here, we present the Spotsizer software tool for automated colony size measurements in images of robotically arrayed microbial colonies. Spotsizer features a convenient graphical user interface (GUI), has both single-image and batch-processing capabilities, and works with multiple input image formats and different colony grid types. We demonstrate how Spotsizer can be used for high-throughput quantitative analysis of fission yeast growth. The user-friendly Spotsizer tool provides rapid, accurate, and robust quantitative analyses of microbial growth in a high-throughput format. Spotsizer is freely available at https://data.csiro.au/dap/landingpage?pid=csiro:15330 under a proprietary CSIRO license.

We describe an investigation into how Massey University's Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University's pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder's native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .

Introduction: While Cumulus - a semi-automated method for measuring breast density - is utilised extensively in research, it is labour-intensive and unsuitable for screening programmes that require an efficient and valid measure on which to base screening recommendations. We develop an automated method to measure breast density (AutoDensity) and compare it to Cumulus in terms of association with breast cancer risk and breast cancer screening outcomes., Methods: AutoDensity automatically identifies the breast area in the mammogram and classifies breast density in a similar way to Cumulus, through a fast, stand-alone Windows or Linux program. Our sample comprised 985 women with screen-detected cancers, 367 women with interval cancers and 4,975 controls (women who did not have cancer), sampled from first and subsequent screening rounds of a film mammography screening programme. To test the validity of AutoDensity, we compared the effect estimates using AutoDensity with those using Cumulus from logistic regression models that tested the association between breast density and breast cancer risk, risk of small and large screen-detected cancers and interval cancers, and screening programme sensitivity (the proportion of cancers that are screen-detected). As a secondary analysis, we report on correlation between AutoDensity and Cumulus measures., Results: AutoDensity performed similarly to Cumulus in all associations tested. For example, using AutoDensity, the odds ratios for women in the highest decile of breast density compared to women in the lowest quintile for invasive breast cancer, interval cancers, large and small screen-detected cancers were 3.2 (95% CI 2.5 to 4.1), 4.7 (95% CI 3.0 to 7.4), 6.4 (95% CI 3.7 to 11.1) and 2.2 (95% CI 1.6 to 3.0) respectively. For Cumulus the corresponding odds ratios were: 2.4 (95% CI 1.9 to 3.1), 4.1 (95% CI 2.6 to 6.3), 6.6 (95% CI 3.7 to 11.7) and 1.3 (95% CI 0.9 to 1.8). Correlation between Cumulus and AutoDensity measures was 0.63 (P < 0.001)., Conclusions: Based on the similarity of the effect estimates for AutoDensity and Cumulus inmodels of breast density and breast cancer risk and screening outcomes, we conclude that AutoDensity is a valid automated method for measuring breast density from digitised film mammograms.

Atlas-based segmentation is a powerful generic technique for automatic delineation of structures in volumetric images. Several studies have shown that multi-atlas segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on volumetric data is time-consuming. Moreover, for many scans or regions within scans, a large number of atlases may not be required to achieve good segmentation performance and may even deteriorate the results. It would therefore be worthwhile to include the decision which and how many atlases to use for a particular target scan in the segmentation process. To this end, we propose two generally applicable multi-atlas segmentation methods, adaptive multi-atlas segmentation (AMAS) and adaptive local multi-atlas segmentation (ALMAS). AMAS automatically selects the most appropriate atlases for a target image and automatically stops registering atlases when no further improvement is expected. ALMAS takes this concept one step further by locally deciding how many and which atlases are needed to segment a target image. The methods employ a computationally cheap atlas selection strategy, an automatic stopping criterion, and a technique to locally inspect registration results and determine how much improvement can be expected from further registrations. AMAS and ALMAS were applied to segmentation of the heart in computed tomography scans of the chest and compared to a conventional multi-atlas method (MAS). The results show that ALMAS achieves the same performance as MAS at a much lower computational cost. When the available segmentation time is fixed, both AMAS and ALMAS perform significantly better than MAS. In addition, AMAS was applied to an online segmentation challenge for delineation of the caudate nucleus in brain MRI scans where it achieved the best score of all results submitted to date.

Purpose: A system is presented for automated estimation of progression of interstitial lung disease in serial thoracic CT scans., Methods: The system compares corresponding 2D axial sections from baseline and follow-up scans and concludes whether this pair of sections represents regression, progression, or unchanged disease status. The correspondence between serial CT scans is achieved by intrapatient volumetric image registration. The system classification function is trained with two different feature sets. Features in the first set represent the intensity distribution of a difference image between the baseline and follow-up CT sections. Features in the second set represent dissimilarities computed between the baseline and follow-up images filtered with a bank of general purpose texture filters., Results: In an experiment on 74 scan pairs, the system classification accuracies were 76.1% and 79.5% for the two feature sets, respectively, while the accuracies of two observer radiologist were 78.5% and 82%, respectively. The agreements of the system with the reference standard, measured by weighted kappa statistics, were 0.611 and 0.683 for the two feature sets, respectively., Conclusions: The system employing the second feature set showed good agreement with the reference standard, and its accuracy approached that of two radiologists.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

In many applications of computer-aided detection (CAD) it is not possible to precisely localize lesions or affected areas in images that are known to be abnormal. In this paper a novel approach to computer-aided detection is presented that can deal effectively with such weakly labeled data. Our approach is based on multi-valued dissimilarity measures that retain more information about underlying local image features than single-valued dissimilarities. We show how this approach can be extended by applying it locally as well as globally, and by merging the local and global classification results into an overall opinion about the image to be classified. The framework is applied to the detection of tuberculosis (TB) in chest radiographs. This is the first study to apply a CAD system to a large database of digital chest radiographs obtained from a TB screening program, including normal cases, suspect cases and cases with proven TB. The global dissimilarity approach achieved an area under the ROC curve of 0.81. The combination of local and global classifications increased this value to 0.83.

A computer-aided detection (CAD) system is presented for the localization of interstitial lesions in chest radiographs. The system analyzes the complete lung fields using a two-class supervised pattern classification approach to distinguish between normal texture and texture affected by interstitial lung disease. Analysis is done pixel-wise and produces a probability map for an image where each pixel in the lung fields is assigned a probability of being abnormal. Interstitial lesions are often subtle and ill defined on x-rays and hence difficult to detect, even for expert radiologists. Therefore a new, semiautomatic method is proposed for setting a reference standard for training and evaluating the CAD system. The proposed method employs the fact that interstitial lesions are more distinct on a computed tomography (CT) scan than on a radiograph. Lesion outlines, manually drawn on coronal slices of a CT scan of the same patient, are automatically transformed to corresponding outlines on the chest x-ray, using manually indicated correspondences for a small set of anatomical landmarks. For the texture analysis, local structures are described by means of the multiscale Gaussian filter bank. The system performance is evaluated with ROC analysis on a database of digital chest radiographs containing 44 abnormal and 8 normal cases. The best performance is achieved for the linear discriminant and support vector machine classifiers, with an area under the ROC curve (A(z)) of 0.78. Separate ROC curves are built for classification of abnormalities of different degrees of subtlety versus normal class. Here the best performance in terms of A(z) is 0.90 for differentiation between obviously abnormal and normal pixels. The system is compared with two human observers, an expert chest radiologist and a chest radiologist in training, on evaluation of regions. Each lung field is divided in four regions, and the reference standard and the probability maps are converted into region scores. The system performance does not significantly differ from that of the observers, when the perihilar regions are excluded from evaluation, and reaches A(z) = 0.85 for the system, with A(z) = 0.88 for both observers.