Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN

Medical image segmentation algorithm based on feedback mechanism convolutional neural network

Medical image segmentation plays an important role in radiation oncology, including delineating anatomical structures and detecting abnormalities. Precise segmentation of medical images is essential for accurately contouring organs at risk (OAR) and target volumes to ensure effective and safe radiation therapy. Recently, there has been growing interest in developing Vision Transformer (ViT) or Convolutional Neural Network (CNN) methods for 3D medical image segmentation. These advanced methods are crucial for handling the complex spatial and semantic structure of 3D medical images, which necessitates both large receptive fields and adaptations to varying spatial geometries. However, previous works in both CNNs and ViTs have demonstrated limitations in fully capturing these complexities, highlighting the need for more sophisticated approaches. We introduce MGDC-Net, a Multi-Group Deformable Convolution (MGDC) network for 3D volumetric medical image segmentation. Our MGDC-Net employs deformable convolution operators with learnable spatial offsets to improve attention on semantically important regions. Our approach leverages stable spatial distribution across subjects to enhance semantic learning. We also incorporate transformer components to augment feature learning and reduce inductive biases inherent in traditional CNNs. Our network employs a combination of deformable convolution operators and transformer components to improve feature learning and computational efficiency. MGDC-net was evaluated on three diverse segmentation tasks using public datasets: brain tumor segmentation (BraTS21), CT multi-organ segmentation (FLARE21), and cross-modality MR/CT segmentation (AMOS22). MGDC-Net demonstrated superior performance on the three segmentation tasks, achieving 91.4% DSC on brain tumor segmentation (BraTS21), 94.4% DSC on CT multi-organ segmentation (FLARE21), and 84.1% DSC on cross-modality MR/CT segmentation (AMOS22). Our network also compared favourably with existing methods in terms of computational efficiency. MGDC-Net provides a robust and efficient method for 3D volumetric medical image segmentation, demonstrating significant improvements in segmentation performance across multiple tasks. This network's ability to leverage deformable convolutions and transformer components suggests its potential for broader applications in medical image analysis.

In this paper, we propose and present a novel algorithm for medical image segmentation (MIS). By analyzing the current state-of-the-art related algorithms, we introduce the multi-band active contour model based limit function to make the multilayer segmentation available. With the development of image segmentation technology, the development of medical image segmentation technology also got very big, because there is no find common, accepted effect ideal is suitable for medical image segmentation method, almost existing each kind of segmentation method has application in the field of medical image segmentation. Furtherly, with the optimized aims of being robust to the noise and avoiding the bad effluence on the result, we adopt the kernel method and new initialization curve. This model suffers from low noise robustness, and model algorithm is difficult to achieve. Integrated segmentation technology refers to two or more technology is used, combined with their own advantages, so they can on the accuracy or efficiency to achieve better performance than when using a single. A new penalty term is introduced to improve numerical stability and the step length is increased to improve efficiency. As far as the robustness and effectiveness are concerned, our method is better than the existing medical image segmentation algorithms. Experimental analysis verifies the success of our method.

The rapid advancements in medical imaging technologies have significantly enhanced diagnostic accuracy and clinical decision-making in modern healthcare. Image segmentation and deep learning have emerged as transformative tools among these advancements. This article explores the pivotal role of image segmentation and deep learning in medical imaging, detailing their methodologies, applications, challenges, and future directions. Deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized medical imaging by automating the analysis of complex datasets and improving diagnostic precision. Image segmentation, a fundamental component of medical imaging, allows for delineating specific structures such as organs, tissues, and pathological regions. Together, these technologies have been applied in diverse fields, including oncology, cardiology, neurology, and ophthalmology, enabling applications such as tumor detection, organ segmentation, disease progression monitoring, and treatment planning. However, despite its transformative potential, the integration of deep learning into medical imaging faces several challenges. These include data scarcity, privacy concerns, interpretability issues, and regulatory hurdles. The article discusses various strategies to address these challenges, such as data augmentation, transfer learning, and the development of explainable AI models to ensure transparency and trustworthiness. Evaluation metrics, such as accuracy, sensitivity, specificity, and Dice Similarity Coefficient (DSC), are essential for assessing model performance. Rigorous clinical validation and regulatory approval are crucial to integrating deep learning systems into clinical workflows effectively. Looking ahead, the future of deep learning in medical imaging holds immense promise. Innovations like multimodal imaging, personalized medicine, and AI-driven automation are set to further revolutionize the field, enhancing the efficiency and accuracy of diagnostics. Collaborative efforts between clinicians, researchers, and AI developers will play a vital role in overcoming current limitations and driving progress. This article concludes by emphasizing the transformative potential of deep learning and image segmentation in medical imaging, highlighting their ability to improve diagnostic accuracy, streamline clinical workflows, and ultimately, enhance patient care. By addressing current challenges and continuing to innovate, these technologies are poised to redefine the landscape of medical diagnostics and treatment in the years to come.

Medical images have become an indispensable and important tool for the diagnosis of medical conditions and surgical guidance. As computer vision technology advances, Medical image segmentation technology has effectively assisted clinicians in making accurate diagnoses and providing personalized treatment. In this paper, some excellent medical image segmentation methods in recent years are summarized, and according to the deep learning method (e.g. Convolutional Neural Network (CNN), U- net, etc.), and traditional methods (such as active contour model, threshold segmentation model, etc.) are sorted out. This paper compares various image segmentation methods, analyzes their similarities and differences, and summarizes and looks forward to the future development of medical image segmentation technology. With the continuous advancement of computer vision models, medical image segmentation is expected to become increasingly accurate and efficient. This will significantly enhance the speed and accuracy of medical image processing, helping doctors to better identify and analyze diseases, thereby providing more accurate clinical diagnoses and treatment plans. With these technological advancements, future medical image segmentation will not only handle more complex images but also enable more intelligent and automated analysis, offering strong support for clinical practice.

Medical image segmentation plays a dominant role in medical image analysis and clinical research. As an effective method for image segmentation, pulse-coupled neural networks (PCNN) has its own limitation that the values of the parameters have a strong effect on its performance when it is used to segment the image. This paper proposed a new method for medical image segmentation using the self-adaptive PCNN model. In this method, we combined the searching capabilities of ant colony optimization (ACO) algorithm in the solution space with the biological characteristics of PCNN, to find the optimal values of PCNN’s parameters for each input image. Moreover, the search process of the ACO algorithm was divided into the local searching and the global searching to accelerate the speed of the ASO’s convergence. Based on the above work, a new automatic method for the image segmentation, namely ACO-PCNN, was presented. Lastly, four pairs of different MR medical images, including transaxial, sagittal, coronal...

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.

2D medical image segmentation via learning multi-scale contextual dependencies.

Background and Objectives: medical image Segmentation is a challenging task due to low contrast between Region of Interest and other textures, hair artifacts in dermoscopic medical images, illumination variations in images like Chest-Xray and various imaging acquisition conditions.Methods: In this paper, we have utilized a novel method based on Convolutional Neural Networks (CNN) for medical image Segmentation and finally, compared our results with two famous architectures, include U-net and FCN neural networks. For loss functions, we have utilized both Jaccard distance and Binary-crossentropy and the optimization algorithm that has used in this method is SGD+Nestrov algorithm. In this method, we have used two preprocessing include resizing image’s dimensions for increasing the speed of our process and Image augmentation for improving the results of our network. Finally, we have implemented threshold technique as postprocessing on the outputs of neural network to improve the contrast of images. We have implemented our model on the famous publicly, PH2 Database, toward Melanoma lesion segmentation and chest Xray images because as we have mentioned, these two types of medical images contain hair artifacts and illumination variations and we are going to show the robustness of our method for segmenting these images and compare it with the other methods.Results: Experimental results showed that this method could outperformed two other famous architectures, include Unet and FCN convolutional neural networks. Additionally, we could improve the performance metrics that have used in dermoscopic and Chest-Xray segmentation which used before.Conclusion: In this work, we have proposed an encoder-decoder framework based on deep convolutional neural networks for medical image segmentation on dermoscopic and Chest-Xray medical images. Two techniques of image augmentation, image rotation and horizontal flipping on the training dataset are performed before feeding it to the network for training. The predictions produced from the model on test images were postprocessed using the threshold technique to remove the blurry boundaries around the predicted lesions.

Convolutional neural networks (CNNs) have obtained enormous success in image segmentation, which is substantial in many clinical treatments. Even though CNNs have achieved state-of-the-art performances, most researches on semantic segmentation using the deep learning methods are in the field of computer vision, so the research on medical images is much less mature than that of natural images, especially, in the field of 3D image segmentation. Our experiments on CNN segmentation models demonstrated that, with modifications and tuning in network architecture and parameters, modified models would show better performances in the selected task, especially with limited training dataset and hardware. We have selected the 3D liver segmentation as our goal and presented a pathway to select a state-of-the-art CNN model and improve it for our specific task and data. Our modifications include the architecture, optimization algorithm, activation functions and the number of convolution filters. With the designed network, we used relatively less training data than other segmentation methods. The direct output of our network, with no further post-processing, resulted in the dice score of ~99 in training and ~95 in validation images in 3D liver segmentation, which is comparable to the state-of-the-art models that used more training images and post-processing. The proposed approach can be easily adapted to other medical image segmentation tasks.

Medical image segmentation is a critical step in many imaging applications. Automatic segmentation has gained extensive concern using a convolutional neural network (CNN). However, the traditional CNN-based methods fail to extract global and long-range contextual information due to local convolution operation. Transformer overcomes the limitation of CNN-based models. Inspired by the success of transformers in computer vision (CV), many researchers focus on designing the transformer-based U-shaped method in medical image segmentation. The transformer-based approach cannot effectively capture the fine-grained details. This paper proposes a dual encoder network with transformer-CNN for multi-organ segmentation. The new segmentation framework takes full advantage of CNN and transformer to enhance the segmentation accuracy. The Swin-transformer encoder extracts global information, and the CNN encoder captures local information. We introduce fusion modules to fuse convolutional features and the sequence of features from the transformer. Feature fusion is concatenated through the skip connection to smooth the decision boundary effectively. We extensively evaluate our method on the synapse multi-organ CT dataset and the automated cardiac diagnosis challenge (ACDC) dataset. The results demonstrate that the proposed method achieves Dice similarity coefficient (DSC) metrics of 80.68% and 91.12% on the synapse multi-organ CT and ACDC datasets, respectively. We perform the ablation studies on the ACDC dataset, demonstrating the effectiveness of critical components of our method. Our results match the ground-truth boundary more consistently than the existing models. Our approach gains more accurate results on challenging 2D images for multi-organ segmentation. Compared with the state-of-the-art methods, our proposed method achieves superior performance in multi-organ segmentation tasks. Graphical Abstract The key process in medical image segmentation.

Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions.

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.

Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues. However, compared to natural images, medical images present unique challenges, such as low resolution, poor contrast, inconsistency, and scattered target regions. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements. In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. This paper categorizes, reviews, and summarizes the current representative methods and research status in the field of medical image segmentation. A comparative analysis of relevant experiments is presented, along with an introduction to commonly used public datasets, performance evaluation metrics, and loss functions in medical image segmentation. Finally, potential future research directions and development trends in this field are predicted and analyzed.

This literature review attempts to provide a brief overview of some of the most common segmentation techniques, and a comparison between them.It discusses Graph based methods, Medical image segmentation research papers and Color Image based Segmentation Techniques. With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques starting from graph based approach to color image segmentation and medical image segmentation, which covers the application of both techniques, are reviewed.Information about open source software packages for image segmentation and standard databases are provided. Finally, summaries and review of research work for image segmentation techniques along with quantitative comparisons for assessing the segmentation results with different parameters are represented in tabular format, which are the extracts of many research papers. Index Terms—Graph based segmentation technique, medical image segmentation, color image segmentation, watershed (WS) method, F-measure, computerized tomography (CT) images I. Introduction Image segmentation is the process of separating or grouping an image into different parts. There are currently many different ways of performing image segmentation, ranging from the simple thresholding method to advanced color image segmentation methods. These parts normally correspond to something that humans can easily separate and view as individual objects. Computers have no means of intelligently recognizing objects, and so many different methods have been developed in order to segment images. The segmentation process in based on various features found in the image. This might be color information, boundaries or segment of an image. The aim of image segmentation is the domain-independent partition of the image into a set of regions, which are visually distinct and uniform with respect to some property, such as grey level, texture or colour. Segmentation can be considered the first step and key issue in object recognition, scene understanding and image understanding. Its application area varies from industrial quality control to medicine, robot navigation, geophysical exploration, military applications, etc. In all these areas, the quality of the final results depends largely on the quality of the segmentation. In this review paper we will discuss on graph based segmentation techniques, color image segmentation techniques and medical image segmentation, which is the real time application and very important field of research. The mathematical details are avoided for simplicity.