Interactive energy minimizing segmentation frameworks

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A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of a state-of-the-art backbone AI for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities

A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of a state-of-the-art backbone AI for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities

Trustworthy Deep Learning for Medical Image Segmentation

Despite the recent success of deep learning methods at achieving new state-of-the-art accuracy for medical image segmentation, some major limitations are still restricting their deployment into clinics. One major limitation of deep learning-based segmentation methods is their lack of robustness to variability in the image acquisition protocol and in the imaged anatomy that were not represented or were underrepresented in the training dataset. This suggests adding new manually segmented images to the training dataset to better cover the image variability. However, in most cases, the manual segmentation of medical images requires highly skilled raters and is time-consuming, making this solution prohibitively expensive. Even when manually segmented images from different sources are available, they are rarely annotated for exactly the same regions of interest. This poses an additional challenge for current state-of-the-art deep learning segmentation methods that rely on supervised learning and therefore require all the regions of interest to be segmented for all the images to be used for training. This thesis introduces new mathematical and optimization methods to mitigate those limitations.Comment: PhD thesis successfully defended on 1st July 2022. Examiners: Prof Sotirios Tsaftaris and Dr Wenjia Ba

Multiple Density Maps Information Fusion for Effectively Assessing Intensity Pattern of Lifelogging Physical Activity

Physical activity (PA) measurement is a crucial task in healthcare technology aimed at monitoring the progression and treatment of many chronic diseases. Traditional lifelogging PA measures require relatively high cost and can only be conducted in controlled or semi-controlled environments, though they exhibit remarkable precision of PA monitoring outcomes. Recent advancement of commercial wearable devices and smartphones for recording one’s lifelogging PA has popularized data capture in uncontrolled environments. However, due to diverse life patterns and heterogeneity of connected devices as well as the PA recognition accuracy, lifelogging PA data measured by wearable devices and mobile phones contains much uncertainty thereby limiting their adoption for healthcare studies. To improve the feasibility of PA tracking datasets from commercial wearable/mobile devices, this paper proposes a lifelogging PA intensity pattern decision making approach for lifelong PA measures. The method is to firstly remove some irregular uncertainties (IU) via an Ellipse fitting model, and then construct a series of monthly based hour-day density map images for representing PA intensity patterns with regular uncertainties (RU) on each month. Finally it explores Dempster-Shafer theory of evidence fusing information from these density map images for generating a decision making model of a final personal lifelogging PA intensity pattern. The approach has significantly reduced the uncertainties and incompleteness of datasets from third party devices. Two case studies on a mobile personalized healthcare platform MHA [1] connecting the mobile app Moves are carried out. The results indicate that the proposed approach can improve effectiveness of PA tracking devices or apps for various types of people who frequently use them as a healthcare indicator

A Neutrosophic Approach Based on TOPSIS Method to Image Segmentation

Neutrosophic set (NS) is a formal framework proposed recently. NS can not only describe the incomplete information in the decision-making system but also depict the uncertainty and inconsistency, so it has applied successfully in several fields such as risk assessment, fuzzy decision and image segmentation. In this paper, a new neutrosophic approach based on TOPSIS method, which can make full use of NS information, is proposed to separate the graphics. Firstly, the image is transformed into the NS domain. Then, two operations, a modified alpha-mean and the beta-enhancement operations are used to enhance image edges and to reduce uncertainty. At last, the segmentation is achieved by the TOPSIS method and the modified fuzzy c-means (FCM). Simulated images and real images are illustrated that the proposed method is more effective and accurate in image segmentation

Advances in Detection and Classification of Underwater Targets using Synthetic Aperture Sonar Imagery

In this PhD thesis, the problem of underwater mine detection and classification using synthetic aperture sonar (SAS) imagery is considered. The automatic detection and automatic classification (ADAC) system is applied to images obtained by SAS systems. The ADAC system contains four steps, namely mine-like object (MLO) detection, image segmentation, feature extraction, and mine type classification. This thesis focuses on the last three steps. In the mine-like object detection step, a template-matching technique based on the a priori knowledge of mine shapes is applied to scan the sonar imagery for the detection of MLOs. Regions containing MLOs are called regions of interest (ROI). They are extracted and forwarded to the subsequent steps, i.e. image segmentation and feature extraction. In the image segmentation step, a modified expectation-maximization (EM) approach is proposed. For the sake of acquiring the shape information of the MLO in the ROI, the SAS images are segmented into highlights, shadows, and backgrounds. A generalized mixture model is adopted to approximate the statistics of the image data. In addition, a Dempster-Shafer theory-based clustering technique is used to consider the spatial correlation between pixels so that the clutters in background regions can be removed. Optimal parameter settings for the proposed EM approach are found with the help of quantitative numerical studies. In the feature extraction step, features are extracted and will be used as the inputs for the mine type classification step. Both the geometrical features and the texture features are applied. However, there are numerous features proposed to describe the object shape and the texture in the literature. Due to the curse of dimensionality, it is indispensable to do the feature selection during the design of an ADAC system. A sophisticated filter method is developed to choose optimal features for the classification purpose. This filter method utilizes a novel feature relevance measure that is a combination of the mutual information, the modified Relief weight, and the Shannon entropy. The selected features demonstrate a higher generalizability. Compared with other filter methods, the features selected by our method can lead to superior classification accuracy, and their performance variation over different classifiers is decreased. In the mine type classification step, the prediction of the types of MLO is considered. In order to take advantage of the complementary information among different classifiers, a classifier combination scheme is developed in the framework of the Dempster-Shafer theory. The outputs of individual classifiers are combined according to this classifier combination scheme. The resulting classification accuracy is better than those of individual classifiers. All of the proposed methods are evaluated using SAS data. Finally, conclusions are drawn, and some suggestions about future works are proposed as well