Medical Image Segmentation by Deep Convolutional Neural Networks

Medical image segmentation is a fundamental and critical step for medical image analysis. Due to the complexity and diversity of medical images, the segmentation of medical images continues to be a challenging problem. Recently, deep learning techniques, especially Convolution Neural Networks (CNNs) have received extensive research and achieve great success in many vision tasks. Specifically, with the advent of Fully Convolutional Networks (FCNs), automatic medical image segmentation based on FCNs is a promising research field. This thesis focuses on two medical image segmentation tasks: lung segmentation in chest X-ray images and nuclei segmentation in histopathological images. For the lung segmentation task, we investigate several FCNs that have been successful in semantic and medical image segmentation. We evaluate the performance of these different FCNs on three publicly available chest X-ray image datasets. For the nuclei segmentation task, since the challenges of this task are difficulty in segmenting the small, overlapping and touching nuclei, and limited ability of generalization to nuclei in different organs and tissue types, we propose a novel nuclei segmentation approach based on a two-stage learning framework and Deep Layer Aggregation (DLA). We convert the original binary segmentation task into a two-step task by adding nuclei-boundary prediction (3-classes) as an intermediate step. To solve our two-step task, we design a two-stage learning framework by stacking two U-Nets. The first stage estimates nuclei and their coarse boundaries while the second stage outputs the final fine-grained segmentation map. Furthermore, we also extend the U-Nets with DLA by iteratively merging features across different levels. We evaluate our proposed method on two public diverse nuclei datasets. The experimental results show that our proposed approach outperforms many standard segmentation architectures and recently proposed nuclei segmentation methods, and can be easily generalized across different cell types in various organs

Automated polyp segmentation based on a multi-distance feature dissimilarity-guided fully convolutional network

Colorectal malignancies often arise from adenomatous polyps, which typically begin as solitary, asymptomatic growths before progressing to malignancy. Colonoscopy is widely recognized as a highly efficacious clinical polyp detection method, offering valuable visual data that facilitates precise identification and subsequent removal of these tumors. Nevertheless, accurately segmenting individual polyps poses a considerable difficulty because polyps exhibit intricate and changeable characteristics, including shape, size, color, quantity and growth context during different stages. The presence of similar contextual structures around polyps significantly hampers the performance of commonly used convolutional neural network (CNN)-based automatic detection models to accurately capture valid polyp features, and these large receptive field CNN models often overlook the details of small polyps, which leads to the occurrence of false detections and missed detections. To tackle these challenges, we introduce a novel approach for automatic polyp segmentation, known as the multi-distance feature dissimilarity-guided fully convolutional network. This approach comprises three essential components, i.e., an encoder-decoder, a multi-distance difference (MDD) module and a hybrid loss (HL) module. Specifically, the MDD module primarily employs a multi-layer feature subtraction (MLFS) strategy to propagate features from the encoder to the decoder, which focuses on extracting information differences between neighboring layers' features at short distances, and both short and long-distance feature differences across layers. Drawing inspiration from pyramids, the MDD module effectively acquires discriminative features from neighboring layers or across layers in a continuous manner, which helps to strengthen feature complementary across different layers. The HL module is responsible for supervising the feature maps extracted at each layer of the network to improve prediction accuracy. Our experimental results on four challenge datasets demonstrate that the proposed approach exhibits superior automatic polyp performance in terms of the six evaluation criteria compared to five current state-of-the-art approaches

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Recent Progress in Transformer-based Medical Image Analysis

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.Comment: Computers in Biology and Medicine Accepte