A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality

Performance and robustness of regional image segmentation driven by selected evolutionary and genetic algorithms: Study on MR articular cartilage images

The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur's entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation's robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.Web of Science2217art. no. 633

Studies on deep learning approach in breast lesions detection and cancer diagnosis in mammograms

Breast cancer accounts for the largest proportion of newly diagnosed cancers in women recently. Early diagnosis of breast cancer can improve treatment outcomes and reduce mortality. Mammography is convenient and reliable, which is the most commonly used method for breast cancer screening. However, manual examinations are limited by the cost and experience of radiologists, which introduce a high false positive rate and false examination. Therefore, a high-performance computer-aided diagnosis (CAD) system is significant for lesions detection and cancer diagnosis. Traditional CADs for cancer diagnosis require a large number of features selected manually and remain a high false positive rate. The methods based on deep learning can automatically extract image features through the network, but their performance is limited by the problems of multicenter data biases, the complexity of lesion features, and the high cost of annotations. Therefore, it is necessary to propose a CAD system to improve the ability of lesion detection and cancer diagnosis, which is optimized for the above problems. This thesis aims to utilize deep learning methods to improve the CADs' performance and effectiveness of lesion detection and cancer diagnosis. Starting from the detection of multi-type lesions using deep learning methods based on full consideration of characteristics of mammography, this thesis explores the detection method of microcalcification based on multiscale feature fusion and the detection method of mass based on multi-view enhancing. Then, a classification method based on multi-instance learning is developed, which integrates the detection results from the above methods, to realize the precise lesions detection and cancer diagnosis in mammography. For the detection of microcalcification, a microcalcification detection network named MCDNet is proposed to overcome the problems of multicenter data biases, the low resolution of network inputs, and scale differences between microcalcifications. In MCDNet, Adaptive Image Adjustment mitigates the impact of multicenter biases and maximizes the input effective pixels. Then, the proposed pyramid network with shortcut connections ensures that the feature maps for detection contain more precise localization and classification information about multiscale objects. In the structure, trainable Weighted Feature Fusion is proposed to improve the detection performance of both scale objects by learning the contribution of feature maps in different stages. The experiments show that MCDNet outperforms other methods on robustness and precision. In case the average number of false positives per image is 1, the recall rates of benign and malignant microcalcification are 96.8% and 98.9%, respectively. MCDNet can effectively help radiologists detect microcalcifications in clinical applications. For the detection of breast masses, a weakly supervised multi-view enhancing mass detection network named MVMDNet is proposed to solve the lack of lesion-level labels. MVMDNet can be trained on the image-level labeled dataset and extract the extra localization information by exploring the geometric relation between multi-view mammograms. In Multi-view Enhancing, Spatial Correlation Attention is proposed to extract correspondent location information between different views while Sigmoid Weighted Fusion module fuse diagnostic and auxiliary features to improve the precision of localization. CAM-based Detection module is proposed to provide detections for mass through the classification labels. The results of experiments on both in-house dataset and public dataset, [email protected] and [email protected] (recall rate@average number of false positive per image), demonstrate MVMDNet achieves state-of-art performances among weakly supervised methods and has robust generalization ability to alleviate the multicenter biases. In the study of cancer diagnosis, a breast cancer classification network named CancerDNet based on Multi-instance Learning is proposed. CancerDNet successfully solves the problem that the features of lesions are complex in whole image classification utilizing the lesion detection results from the previous chapters. Whole Case Bag Learning is proposed to combined the features extracted from four-view, which works like a radiologist to realize the classification of each case. Low-capacity Instance Learning and High-capacity Instance Learning successfully integrate the detections of multi-type lesions into the CancerDNet, so that the model can fully consider lesions with complex features in the classification task. CancerDNet achieves the AUC of 0.907 and AUC of 0.925 on the in-house and the public datasets, respectively, which is better than current methods. The results show that CancerDNet achieves a high-performance cancer diagnosis. In the works of the above three parts, this thesis fully considers the characteristics of mammograms and proposes methods based on deep learning for lesions detection and cancer diagnosis. The results of experiments on in-house and public datasets show that the methods proposed in this thesis achieve the state-of-the-art in the microcalcifications detection, masses detection, and the case-level classification of cancer and have a strong ability of multicenter generalization. The results also prove that the methods proposed in this thesis can effectively assist radiologists in making the diagnosis while saving labor costs