TABNet: A Triplet Augmentation Self-recovery framework with Boundary-aware Pseudo-labels for scribble-based medical image segmentation

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for various imaging related tasks such as artificial image generation to support AI training. GANs are especially useful for medical imaging-related tasks where training datasets are usually limited in size and heavily imbalanced against the diseased class. We present a systematic review, following the PRISMA guidelines, of recent GAN architectures used for medical image analysis to help the readers in making an informed decision before employing GANs in developing medical image classification and segmentation models. We have extracted 54 papers that highlight the capabilities and application of GANs in medical imaging from January 2015 to August 2020 and inclusion criteria for meta-analysis. Our results show four main architectures of GAN that are used for segmentation or classification in medical imaging. We provide a comprehensive overview of recent trends in the application of GANs in clinical diagnosis through medical image segmentation and classification and ultimately share experiences for task-based GAN implementations.

Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans. Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image. Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN). The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature. This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express. Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm. The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel. Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed. The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved. Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.

DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation

In this paper, a novel level set approach is proposed for segmentation of medical ultrasound images. Considering the speckle noise and low contrast of medical ultrasound images, we add an extra stopping term into the formulation of level set evolution without re-initialization (LSEWR). Compared with traditional active contour models, the proposed level set approach has more flexible initialization and larger capture range. It is insensitive to the initial contour and larger time step can be used. The initial contour can be easily initialized as a circle or rectangular, thus achieving semi-automatic segmentation of ultrasound medical images. The experimental results show that the proposed method can be used for semi-automatic and high-quality segmentation of medical ultrasound images.

– Medical image segmentation is considered the most precarious element in the analysis and processing of real-life images in the clinical sector. Actually, segmentation effects affect the subsequent procedures of image evaluation, life object illustration and description, feature dimensions and the subsequent considerable task levels such as object categorization. In that case, medical image segmentation is the most essential and crucial aspect for aiding the visualization, delineation and depiction of regions of interest for any particular image. The aspect of physical segmentation of a picture is not just challenging and time-consuming task to do, but also problematic to assure accuracy considering the wide-range image modalities and unguided image quantities which have to be observed. In that case, it now becomes fundamental to evaluate the present approaches of image segmentation based on the application of computing algorithms which require the interaction of users with evaluating medical images. In the process of image segmentation, an anatomic classification requires to be extracted or defined to be projected effectively and independently. In that case, this contribution is focused on image segmentation to extract details for decision-making in the clinical sector. The paper presents generalized and relative techniques that have been categorized into three groups: pixel-centre, edge-centred and region-centred techniques. The paper also provides a highlight of the strengths and weaknesses of these techniques in reference to the appropriateness of a wide-range application in medical image segmentation.

Ultrasound imaging can distinctly display the morphology and structure of internal organs within the human body, enabling the examination of organs like the breast, liver, and thyroid. It can identify the locations of tumors, nodules, and other lesions, thereby serving as an efficacious tool for treatment detection and rehabilitation evaluation. Typically, the attending physician is required to manually demarcate the boundaries of lesion locations, such as tumors, in ultrasound images. Nevertheless, several issues exist. The high noise level in ultrasound images, the degradation of image quality due to the impact of surrounding tissues, and the influence of the operator’s experience and proficiency on the determination of lesion locations can all contribute to a reduction in the accuracy of delineating the boundaries of lesion sites. In the wake of the advancement of deep learning, its application in medical image segmentation is becoming increasingly prevalent. For instance, while the U-Net model has demonstrated a favorable performance in medical image segmentation, the convolution layers of the traditional U-Net model are relatively simplistic, leading to suboptimal extraction of global information. Moreover, due to the significant noise present in ultrasound images, the model is prone to interference. In this research, we propose an Attention Residual Network model (ARU-Net). By incorporating residual connections within the encoder section, the learning capacity of the model is enhanced. Additionally, a spatial hybrid convolution module is integrated to augment the model’s ability to extract global information and deepen the vertical architecture of the network. During the feature fusion stage of the skip connections, a channel attention mechanism and a multi-convolutional self-attention mechanism are respectively introduced to suppress noisy points within the fused feature maps, enabling the model to acquire more information regarding the target region. Finally, the predictive efficacy of the model was evaluated using publicly accessible breast ultrasound and thyroid ultrasound data. The ARU-Net achieved mean Intersection over Union (mIoU) values of 82.59% and 84.88%, accuracy values of 97.53% and 96.09%, and F1-score values of 90.06% and 89.7% for breast and thyroid ultrasound, respectively.

Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings. To address these issues, we propose a novel deep learning-based approach, MIPC-Net, designed for precise boundary segmentation in medical images. Our approach, inspired by radiologists' working patterns, introduces two distinct modules: 1. Mutual Inclusion of Position and Channel Attention (MIPC) Module: To improve boundary segmentation precision, we present the MIPC module. This module enhances the focus on channel information while extracting position features and vice versa, effectively enhancing the segmentation of boundaries in medical images. 2. Skip-Residue Module: To optimize the restoration of medical images, we introduce Skip-Residue, a global residual connection. This module improves the integration of the encoder and decoder by filtering out irrelevant information and recovering the most crucial information lost during the feature extraction process. We evaluate the performance of MIPC-Net on three publicly accessible datasets: Synapse, ISIC2018-Task, and Segpc. The evaluation uses metrics such as the Dice coefficient (DSC) and Hausdorff Distance (HD). Our ablation study confirms that each module contributes to the overall improvement of segmentation quality. Notably, with the integration of both modules, our model outperforms state-of-the-art methods across all metrics. Specifically, MIPC-Net achieves a 2.23 mm reduction in Hausdorff Distance on the Synapse dataset, highlighting the model's enhanced capability for precise image boundary segmentation. The introduction of the novel MIPC and Skip-Residue modules significantly improves feature extraction accuracy, leading to better boundary recognition in medical image segmentation tasks. Our approach demonstrates substantial improvements over existing methods, as evidenced by the results on benchmark datasets.

Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm

Medical image segmentation, the process of precisely delineating regions of interest (e.g. organs, lesions, cells) within medical images, is a pivotal technique in medical image analysis. It finds widespread application in computer‐aided diagnosis, surgical planning, radiation therapy, and pathological analysis, thus playing a crucial role in enabling precision medicine and enhancing the quality of clinical care. Traditional medical image segmentation methods often rely on hand‐crafted features and rule‐based approaches, which struggle to handle the inherent complexity and variability of medical imagery, leading to limitations in segmentation accuracy and robustness. Recently, deep learning methodologies, driven by their powerful capabilities in automatic feature learning and non‐linear modelling, have overcome the limitations of traditional methods and achieved significant advancements in the field of medical image segmentation. This review provides a comprehensive overview and summary of recent progress in deep learning‐based medical image segmentation, with a particular focus on fully supervised learning paradigms leveraging convolutional neural networks, transformers, and the segment anything model. We delve into the underlying principles, network architectures, advantages, and limitations of these approaches. Furthermore, we systematically compare their performance across diverse imaging modalities, anatomical structures, and pathological targets. We also present a curated compilation of commonly used datasets, evaluation metrics, and loss functions relevant to medical image segmentation. Finally, we discuss future research directions and potential challenges, offering insights into the evolving landscape of this critical field.

Segmentation on medical image requires good quality due to affect the interpretation and diagnosis of medical experts. On medical image segmentation, there is merging phase to increase the quality of the segmentation result. However, stopping criteria on merging phase was determined manually by medical experts. It implied the subjectivity of segmentation result. To increase the objectivity of segmentation result, a method to automate merging phase on medical image segmentation is required. Therefore, we propose a novel method on medical image segmentation which combine two-stage SOM and T-cluster method. Experiments were performed on dental panoramic as medical image sample and evaluated by using segmentation quality formula. Experiments show that the proposed method can perform segmentation on dental panoramic image automatically and objectively with the best average of segmentation quality value is 4,40.
 Index Terms—dental panoramic image, image segmentation, medical image, Self-Organizing Map, T-cluster

The rapid advancement of prompt-based models in natural language processing and image generation has revolutionized the field of image segmentation. The introduction of the Segment Anything Model (SAM) has further invigorated this domain with its unprecedented versatility. However, its applicability to medical image segmentation remains uncertain due to significant disparities between natural and medical images, which demand careful consideration. This study comprehensively analyzes recent efforts to adapt SAM for medical image segmentation, including empirical benchmarking and methodological refinements aimed at bridging the gap between SAM's capabilities and the unique challenges of medical imaging. Furthermore, we explore future directions for SAM in this field. While direct application of SAM to complex, multimodal, and multi-target medical datasets may not yet yield optimal results, insights from these efforts provide crucial guidance for developing foundational models tailored to the intricacies of medical image analysis. Despite existing challenges, SAM holds considerable potential to demonstrate its unique advantages and robust capabilities in medical image segmentation in the near future.

Enhancing retinal blood vessel segmentation in medical images using combined segmentation modes extracted by DBSCAN and morphological reconstruction

Endoscopic imaging is an important tool for living detection, and it provides first-hand information for pathological diagnosis, such as digestive tract and respiratory tract disease. However, the imaging quality of endoscopic images faces many challenges due to the particularity of human internal environment. For example, water vapor and blood stains will block the lens, and unstable movements of the endoscope equipment will cause serious jitter of the field of view. In addition, the imaging range of the endoscope is limited, and the jitter often caused the problem of target loss. Based on the above problems, the algorithm on target segmentation and tracking of medical endoscopic images is proposed to assist doctors in real-time analysis, and extract accurate information in the harsh observation environment. Target segmentation and tracking are two important tasks in computer vision. Most tracking algorithms use target detection to ensure real-time performance, however, the combination of segmentation and tracking is conducive to improving the identification accuracy. In addition, most segmentation and tracking focus on the real scenarios, the related datasets include DAVIS (Densely-Annotated Video Segmentation), OTB (Online Objective Tracking Benchmark) and VOT (Visual Object Tracking). However, the endoscope images are quite different due to the small detection range and difficult control of the viewing angle. Inspire by SAT (State-Aware Tracker), AlexNet is used to perform the tracking task to ensure real-time tracking, and excessive receptive field and random sampling are introduced to adapt to the characteristic of fast speed and irregular direction of target movements in the endoscope scene. In addition, this paper uses the training enhancement of the first-frame deformation to simulate the process of target appearance. VOT and DAVIS are used for pre-training, and the Hyper Kvasir dataset, which is the dataset on endoscopic polyp, is used for semi-supervised training and performance evaluation. The Dice of the proposed algorithm is better than the comparison algorithm. The algorithm on target segmentation and tracking of medical endoscopic images can monitor the images in the entire process, and give doctors reminders and information confirmation. The fully automatic computer vision algorithm can make full use of every detail in the endoscopic surgery, and it can free doctors form the high-intensity image monitoring, so that they can put their time and energy into more important work.

Need for medical imaging has immensely increased to diagnose functionality of organs and tissues. Application of magnetic resonance imaging (MRI) or computerized tomography scan (CT scan) has automated the process of disease prediction which otherwise would need manual intervention of experienced physicians. Critical transplants and tumor diagnosis of kidney, heart and liver could be very challenging without the application of high-resolution medical imaging. Semantic image segmentation helps to identify features within the image and label them to demarcate the background from organs and tumors formed on them. Current proposed model is built by applying image segmentation using deep learning models by training 3D image information obtained from KITS-19 using CNN U-Net architecture to segregate essential tumor details from the kidney background images. Considering the evaluation of medical condition based on imagery is very challenging due to lack of pre-processing methods to handle unbalanced nature of class distribution which is common in medical imaging. In the current research, we apply state-of-the-art convolution neural network and deep learning models to predict the tumor in MRI imagery. We performed evaluation of a subset of 60 CT scans with slice thickness values obtained from KiTS19 dataset. We also compared the image segmentation model with BRATS’2013 challenging dataset also to validate the prediction of tumor in brain MRI scan images. Performance evaluation of kidney and tumor features obtained through threefold cross validation comparing with ground truth gave us dice coefficient score of 0.96 while tumor segmentation prediction was close to 0.80. We believe the performance can be increased through higher GPU memory requirements, which was a limitation to our existing GPU hardware. Nevertheless, the possibility of applying improved deep learning model based on existing results and can be a promising step forward in medical imaging segmentation.

The increasing demand for segmentation of lesions in medical images necessitates research on automatic segmentation. Manual segmentation is inefficient due to training time and energy constraints. Deep learning-based image segmentation technology can improve efficiency and aid in diagnosing conditions. This technology provides accurate and detailed data support for clinical medicine, making it a crucial field in medical image processing. This essay introduces image segmentation and its classification, and explains the differences between two segmentation methods, semantic segmentation and instance segmentation and their respective application fields. Additionally, it introduces several well-known deep neural networks for segmenting medical images using deep learning. Regarding the models, this article introduces the structure and characteristics of each model as well as their respective advantages and disadvantages. This essay also introduces examples of deep learning using different deep neural network models to segment specific medical images, including image segmentation based on FCN for the heart and U-Net for the kidneys.