MFHARFNet: multi-branch feature hybrid and adaptive receptive field network for image segmentation

Objective. Cardiovascular disease (CVD) is a group of diseases affecting cardiac and blood vessels, and short-axis cardiac magnetic resonance (CMR) images are considered the gold standard for the diagnosis and assessment of CVD. In CMR images, accurate segmentation of cardiac structures (e.g. left ventricle) assists in the parametric quantification of cardiac function. However, the dynamic beating of the heart renders the location of the heart with respect to other tissues difficult to resolve, and the myocardium and its surrounding tissues are similar in grayscale. This makes it challenging to accurately segment the cardiac images. Our goal is to develop a more accurate CMR image segmentation approach. Approach. In this study, we propose a regional perception and multi-scale feature fusion network (RMFNet) for CMR image segmentation. We design two regional perception modules, a window selection transformer (WST) module and a grid extraction transformer (GET) module. The WST module introduces a window selection block to adaptively select the window of interest to perceive information, and a windowed transformer block to enhance global information extraction within each feature window. The WST module enhances the network performance by improving the window of interest. The GET module grids the feature maps to decrease the redundant information in the feature maps and enhances the extraction of latent feature information of the network. The RMFNet further introduces a novel multi-scale feature extraction module to improve the ability to retain detailed information. Main results. The RMFNet is validated with experiments on three cardiac data sets. The results show that the RMFNet outperforms other advanced methods in overall performance. The RMFNet is further validated for generalizability on a multi-organ data set. The results also show that the RMFNet surpasses other comparison methods. Significance. Accurate medical image segmentation can reduce the stress of radiologists and play an important role in image-guided clinical procedures.

MC-DC: An MLP-CNN Based Dual-path Complementary Network for Medical Image Segmentation

LCP-Net: A local context-perception deep neural network for medical image segmentation

Medical image segmentation plays an important role in medical diagnosis, and has received extensive attention in recent years. A large number of convolutional neural network based methods have been proposed to achieve accurate segmentation results. Dice loss is the most popular loss function for medical image segmentation tasks. However, we found that Dice loss suffers from abnormal gradient changes, which causes the loss function to be unstable and difficult to converge. Therefore, we propose an gradient-optimized Dice loss (GODC) to solve this problem. GODC corrects the abnormal gradient changes in the segmentation loss, which accelerates the model convergence and can achieve better segmentation performance. Next, we propose a lateral feature alignment module (LFAM). LFAM adopts deformable convolutional network to align the features of different layers on the shortcut connections of U-Net to improve the segmentation performance. Finally, our method achieves state-of-the-art results on the LiTS dataset as well as our collected pancreatic tumor datasets.

Segmenting medical images via explicit–implicit attention aggregation

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.

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.

The imperative development of point-of-care diagnosis for accurate and rapid medical image segmentation, has become increasingly urgent in recent years. Although some pioneering work has applied complex modules to improve segmentation performance, resulting models are often heavy, which is not practical for the modern clinical setting of point-of-care diagnosis. To address these challenges, we propose UltraNet, a state-of-the-art lightweight model that achieves competitive performance in segmenting multiple parts of medical images with the lowest parameters and computational complexity. To extract a sufficient amount of feature information and replace cumbersome modules, the Shallow Focus Float Block (ShalFoFo) and the Dual-stream Synergy Feature Extraction (DuSem) are respectively proposed at both shallow and deep levels. ShalFoFo is designed to capture finer-grained features containing more pixels, while DuSem is capable of extracting distinct deep semantic features from two different perspectives. By jointly utilizing them, the accuracy and stability of UltraNet segmentation results are enhanced. To evaluate performance, UltraNet's generalization ability was assessed on five datasets with different tasks. Compared to UNet, UltraNet reduces the parameters and computational complexity by 46 times and 26 times, respectively. Experimental results demonstrate that UltraNet achieves a state-of-the-art balance among parameters, computational complexity, and segmentation performance. Codes are available at https://github.com/Ziii1/UltraNet .

Explainable multi-module semantic guided attention based network for medical image segmentation

Segmentation is a fundamental task in medical image analysis. Current state-of-the-art Convolutional Neural Networks on medical image segmentation capture local context information using fixed-shape receptive fields and feature detectors with position-invariant weights, which limits the robustness to the variance of input, such as medical objects of variant sizes, shapes, and domains. In order to capture global context information, we propose High-order Attention (HA), a novel attention module with adaptive receptive fields and dynamic weights. HA allows each pixel to has its own global attention map that models its relationship to all other pixels. In particular, HA constructs the attention map through graph transduction and thus captures high relevant context information at high-order. Consequently, feature maps at each position are selectively aggregated as a weighted sum of feature maps at all positions. We further embed the proposed HA module into an efficient encoder-decoder structure for medical image segmentation, namely High-order Attention Network (HANet). Extensive experiments are conducted on four benchmark sets for three tasks, i.e., REFUGE and Drishti-GS1 for optic disc/cup segmentation, DRIVE for blood vessel segmentation, and LUNA for lung segmentation. The results justify the effectiveness of the new attention module for medical image segmentation.

Accurate segmentation is a basic and crucial step for medical image processing and analysis. In the last few years, U-Net, and its variants, have become widely adopted models in medical image segmentation tasks. However, the multiple training parameters of these models determines high computation complexity, which is impractical for further applications. In this paper, by introducing depthwise separable convolution and attention mechanism into U-shaped architecture, we propose a novel lightweight neural network (DSCA-Net) for medical image segmentation. Three attention modules are created to improve its segmentation performance. Firstly, Pooling Attention (PA) module is utilized to reduce the loss of consecutive down-sampling operations. Secondly, for capturing critical context information, based on attention mechanism and convolution operation, we propose Context Attention (CA) module instead of concatenation operations. Finally, Multiscale Edge Attention (MEA) module is used to emphasize multi-level representative scale edge features for final prediction. The number of parameters in our network is 2.2 M, which is 71.6% less than U-Net. Experiment results across four public datasets show the potential and the dice coefficients are improved by 5.49% for ISIC 2018, 4.28% for thyroid, 1.61% for lung and 9.31% for nuclei compared with U-Net.

CASF-Net: Cross-attention and cross-scale fusion network for medical image segmentation

MHSU-Net: A more versatile neural network for medical image segmentation

Medical image segmentation is crucial for improving healthcare outcomes. Convolutional neural networks (CNNs) have been widely applied in medical image analysis; however, their inherent inductive biases limit their ability to capture global contextual information. Vision transformer (ViT) architectures address this limitation by leveraging attention mechanisms to model global relationships; however, they typically require large-scale datasets for effective training, which is challenging in the field of medical imaging due to limited data availability. This study aimed to integrate the advantages of CNN and ViT architectures to improve segmentation performance on small-scale medical image datasets. In this study, we established a U-shaped network architecture based on a Transformer-assisted convolutional neural network (TAC-UNet). The TAC-UNet is primarily composed of a hybrid structure integrating CNN and Transformer components. Specifically, the hybrid architecture follows a dual-path design in which the Transformer branch continuously conveys global contextual information to the CNN backbone. This allows the CNN backbone to enhance its global perception while building on the local features it extracts, thereby improving its ability to comprehend complex image structures. A channel cross-attention (CCA) module is also incorporated as a bridge between the encoder and decoder to better reconcile the semantic discrepancies between them. Detailed experiments on three public datasets were conducted. Specifically, our model was trained on 30 images from the Multi-organ Nucleus Segmentation (MoNuSeg) training dataset, 85 images from the Gland Segmentation (GlaS) training dataset, and 551 images from the Computer Vision Center Colorectal Cancer-Clinic Database (CVC-ClinicDB) dataset. We evaluated the performance of our model on the corresponding test sets. Our TAC-UNet achieved the best Dice scores (80.36%, 90.70%, and 91.81% on the MoNuSeg, GlaS, and CVC-ClinicDB datasets, respectively) of all the models. Compared to other CNN-based, Transformer-based, and hybrid methods, the TAC-UNet demonstrated significantly superior segmentation performance. Our TAC-UNet model showed advanced segmentation performance on small-scale medical image datasets. The detailed experimental results showed the effectiveness of the method. Our model's code is available at: https://github.com/hejlhello/TAC-UNet.

Medical image segmentation using dual-decoder mutual teaching with a mean teacher framework.