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The recognition of different cell compartments, the types of cells, and their interactions is a critical aspect of quantitative cell biology. However, automating this problem has proven to be non-trivial and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. To alleviate this, graphical models are useful due to their ability to make use of prior knowledge and model inter-class dependences. Directed acyclic graphs, such as trees, have been widely used to model top-down statistical dependences as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, we propose polytree graphical models that capture label proximity relations more naturally compared to tree-based approaches. A novel recursive mechanism based on two-pass message passing was developed to efficiently calculate closed-form posteriors of graph nodes on polytrees. The algorithm is evaluated on simulated data and on two publicly available fluorescence microscopy datasets, outperforming directed trees and three state-of-the-art convolutional neural networks, namely, SegNet, DeepLab, and PSPNet. Polytrees are shown to outperform directed trees in predicting segmentation error by highlighting areas in the segmented image that do not comply with prior knowledge. This paves the way to uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement. [ABSTRACT FROM AUTHOR]

Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners, and color charts make lesion segmentation an intricate task. In order to detect the lesion in the presence of these problems, we propose a supervised saliency detection method tailored for dermoscopic images based on the discriminative regional feature integration (DRFI). A DRFI method incorporates multilevel segmentation, regional contrast, property, background descriptors, and a random forest regressor to create saliency scores for each region in the image. In our improved saliency detection method, mDRFI, we have added some new features to regional property descriptors. Also, in order to achieve more robust regional background descriptors, a thresholding algorithm is proposed to obtain a new pseudo-background region. Findings reveal that mDRFI is superior to DRFI in detecting the lesion as the salient object in dermoscopic images. The proposed overall lesion segmentation framework uses detected saliency map to construct an initial mask of the lesion through thresholding and postprocessing operations. The initial mask is then evolving in a level set framework to fit better on the lesion's boundaries. The results of evaluation tests on three public datasets show that our proposed segmentation method outperforms the other conventional state-of-the-art segmentation algorithms and its performance is comparable with most recent approaches that are based on deep convolutional neural networks. [ABSTRACT FROM AUTHOR]

Purpose The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantly. Method The proposed method starts with preprocessing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multiscale dot enhancement filtering prior to nodule detection and feature extraction. Finally, classification of lung nodules is achieved using Support Vector Machine ( SVM) classifier. The feature set consists of intensity, shape (2D and 3D) and texture features, which have been selected to optimize the sensitivity and reduce false positives. In addition to SVM, some other supervised classifiers like K-Nearest-Neighbor ( KNN), Decision Tree and Linear Discriminant Analysis ( LDA) have also been used for performance comparison. The extracted features have also been compared class-wise to determine the most relevant features for lung nodule detection. The proposed system has been evaluated using 850 scans from Lung Image Database Consortium ( LIDC) dataset and k-fold cross-validation scheme. Results The overall sensitivity has been improved compared to the previous methods and false positives per scan have been reduced significantly. The achieved sensitivities at detection and classification stages are 94.20% and 98.15%, respectively, with only 2.19 false positives per scan. Conclusions It is very difficult to achieve high performance metrics using only a single feature class therefore hybrid approach in feature selection remains a better choice. Choosing right set of features can improve the overall accuracy of the system by improving the sensitivity and reducing false positives. [ABSTRACT FROM AUTHOR]

Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into 'high' or 'low immune' subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into 'high' and 'low immune' subgroups with significantly different melanoma specific survival outcomes (log rank test, P < 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests. [Display omitted] • RNA analysis to classify melanomas into immune subgroups is costly and time-consuming. • We compare SOTA MIL methods, showing these subgroups can be identified using WSIs. • 10x resolution input patches improve performance, through a balance of cellular detail and context. • A SSL 'feature extractor', trained with histopathology images, improves performance. • The classified high and low immune subgroups have differing survival outcomes. [ABSTRACT FROM AUTHOR]