EliasHossain/CancerAbstracts huggingface dataset:
Category: Generic
The U-Net architecture is a prominent technique for image segmentation. However, a significant challenge in utilizing this algorithm is the selection of appropriate hyperparameters. In this study, we aimed to address this issue using an evolutionary approach. We conducted experiments on four different geometric datasets (triangle, kite, parallelogram, and square), with 1,000 training samples and 200 test samples. Initially, we performed image segmentation without the evolutionary approach, manually adjusting the U-Net hyperparameters. The average accuracy rates for the geometric images were 0.94463, 0.96289, 0.96962, and 0.93971, respectively. Subsequently, we proposed a hybrid version of the U-Net architecture, incorporating the Grasshopper Optimization Algorithm (GOA) for an evolutionary approach. This method automatically discovered the optimal hyperparameters, resulting in improved image segmentation performance. The average accuracy rates achieved by the proposed method were 0.99418, 0.99673, 0.99143, and 0.99946, respectively, for the geometric images. Comparative analysis revealed that the proposed UNet-GOA approach outperformed the traditional U-Net architecture, yielding higher accuracy rates.
Category: Generic
Prostate cancer is a common disease that seriously endangers the health of middle-aged and elderly men. MRI images are the gold standard for assessing the health status of the prostate region. Segmentation of the prostate region is of great significance for the diagnosis of prostate cancer. In the past, some methods have been used to segment the prostate region, but segmentation accuracy still has room for improvement. This study has proposed a new image segmentation model based on Attention UNet. The model improves Attention UNet by using GN instead of BN, adding dropout to prevent overfitting, introducing the ASPP module, adding channel attention to the attention gate module, and using different channels to output segmentation results of different prostate regions. Finally, we conducted comparative experiments using five existing UNet-based models, and used the dice coefficient as the metric to evaluate the segmentation result. The proposed model achieves dice scores of 0.807 and 0.907 in the transition region and the peripheral region, respectively. The experimental results show that the proposed model is better than other UNet-based models.
Category: Generic
The outbreak of new coronary pneumonia has brought severe health risks to the world. Detection of COVID-19 based on the UNet network has attracted widespread attention in medical image segmentation. However, the traditional UNet model is challenging to capture the long-range dependence of the image due to the limitations of the convolution kernel with a fixed receptive field. The Transformer Encoder overcomes the long-range dependence problem. However, the Transformer-based segmentation approach cannot effectively capture the fine-grained details. We propose a transformer with a double decoder UNet for COVID-19 lesions segmentation to address this challenge, TDD-UNet. We introduce the multi-head self-attention of the Transformer to the UNet encoding layer to extract global context information. The dual decoder structure is used to improve the result of foreground segmentation by predicting the background and applying deep supervision. We performed quantitative analysis and comparison for our proposed method on four public datasets with different modalities, including CT and CXR, to demonstrate its effectiveness and generality in segmenting COVID-19 lesions. We also performed ablation studies on the COVID-19-CT-505 dataset to verify the effectiveness of the key components of our proposed model. The proposed TDD-UNet also achieves higher Dice and Jaccard mean scores and the lowest standard deviation compared to competitors. Our proposed method achieves better segmentation results than other state-of-the-art methods.
Category: Generic
Personalized heart models are widely used to study the mechanisms of cardiac arrhythmias and have been used to guide clinical ablation of different types of arrhythmias in recent years. MRI images are now mostly used for model building. In cardiac modeling studies, the degree of segmentation of the heart image determines the success of subsequent 3D reconstructions. Therefore, a fully automated segmentation is needed. In this paper, we combine U-Net and Transformer as an alternative approach to perform powerful and fully automated segmentation of medical images. On the one hand, we use convolutional neural networks for feature extraction and spatial encoding of inputs to fully exploit the advantages of convolution in detail grasping; on the other hand, we use Transformer to add remote dependencies to high-level features and model features at different scales to fully exploit the advantages of Transformer. The results show that, the average dice coefficients for ACDC and Synapse datasets are 91.72 and 85.46%, respectively, and compared with Swin-Unet, the segmentation accuracy are improved by 1.72% for ACDC dataset and 6.33% for Synapse dataset.
Category: Generic
Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose, detect, and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. To improve the performance of the Att-Unet architecture and maximize the use of the Attention Gate, we propose the PAtt-Unet and DAtt-Unet architectures. PAtt-Unet aims to exploit the input pyramids to preserve the spatial awareness in all of the encoder layers. On the other hand, DAtt-Unet is designed to guide the segmentation of Covid-19 infection inside the lung lobes. We also propose to combine these two architectures into a single one, which we refer to as PDAtt-Unet. To overcome the blurry boundary pixels segmentation of Covid-19 infection, we propose a hybrid loss function. The proposed architectures were tested on four datasets with two evaluation scenarios (intra and cross datasets). Experimental results showed that both PAtt-Unet and DAtt-Unet improve the performance of Att-Unet in segmenting Covid-19 infections. Moreover, the combination architecture PDAtt-Unet led to further improvement. To Compare with other methods, three baseline segmentation architectures (Unet, Unet++, and Att-Unet) and three state-of-the-art architectures (InfNet, SCOATNet, and nCoVSegNet) were tested. The comparison showed the superiority of the proposed PDAtt-Unet trained with the proposed hybrid loss (PDEAtt-Unet) over all other methods. Moreover, PDEAtt-Unet is able to overcome various challenges in segmenting Covid-19 infections in four datasets and two evaluation scenarios.
Category: Generic
The accurate segmentation of retinal vessels images can not only be used to evaluate and monitor various ophthalmic diseases, but also timely reflect systemic diseases such as diabetes and blood diseases. Therefore, the study on segmentation of retinal vessels images is of great significance for the diagnosis of visually threatening diseases. In recent years, especially the convolutional neural networks (CNN) based on UNet and its variant have been widely used in various medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global and long-distance semantic information interaction well due to the local computing characteristics of convolution operation, which limits the development of medical image segmentation tasks. Transformer, currently popular in computer vision, has global computing features, but due to the lack of low-level details, local feature information extraction is insufficient. In this paper, we propose Patches Convolution Attention based Transformer UNet (PCAT-UNet), which is a U-shaped network based on Transformer with a Convolution branch. We use skip connection to fuse the deep and shallow features of both sides. By taking advantage of the complementary advantages of both sides, we can effectively capture the global dependence relationship and the details of the underlying feature space, thus improving the current problems of insufficient extraction of retinal micro vessels feature information and low sensitivity caused by easily predicting of pixels as background. In addition, our method enables end-to-end training and rapid inference. Finally, three publicly available retinal vessels datasets (DRIVE, STARE and CHASE_DB1) were used to evaluate PCAT-UNet. The experimental results show that the proposed PCAT-UNET method achieves good retinal vessel segmentation performance on these three datasets, and is superior to other architectures in terms of AUC, Accuracy and Sensitivity performance indicators. AUC reached 0.9872, 0.9953 and 0.9925, Accuracy reached 0.9622, 0.9796 and 0.9812, Sensitivity reached 0.8576, 0.8703 and 0.8493, respectively. In addition, PCAT-UNET also achieved good results in two other F1-Score and Specificity indicators.
Category: Generic
Medical image segmentation is crucial for medical image processing and the development of computer-aided diagnostics. In recent years, deep Convolutional Neural Networks (CNNs) have been widely adopted for medical image segmentation and have achieved significant success. UNet, which is based on CNNs, is the mainstream method used for medical image segmentation. However, its performance suffers owing to its inability to capture long-range dependencies. Transformers were initially designed for Natural Language Processing (NLP), and sequence-to-sequence applications have demonstrated the ability to capture long-range dependencies. However, their abilities to acquire local information are limited. Hybrid architectures of CNNs and Transformer, such as TransUNet, have been proposed to benefit from Transformer's long-range dependencies and CNNs' low-level details. Nevertheless, automatic medical image segmentation remains a challenging task due to factors such as blurred boundaries, the low-contrast tissue environment, and in the context of ultrasound, issues like speckle noise and attenuation. In this paper, we propose a new model that combines the strengths of both CNNs and Transformer, with network architectural improvements designed to enrich the feature representation captured by the skip connections and the decoder. To this end, we devised a new attention module called Three-Level Attention (TLA). This module is composed of an Attention Gate (AG), channel attention, and spatial normalization mechanism. The AG preserves structural information, whereas channel attention helps to model the interdependencies between channels. Spatial normalization employs the spatial coefficient of the Transformer to improve spatial attention akin to TransNorm. To further improve the skip connection and reduce the semantic gap, skip connections between the encoder and decoder were redesigned in a manner similar to that of the UNet++ dense connection. Moreover, deep supervision using a side-output channel was introduced, analogous to BASNet, which was originally used for saliency predictions. Two datasets from different modalities, a CT scan dataset and an ultrasound dataset, were used to evaluate the proposed UNet architecture. The experimental results showed that our model consistently improved the prediction performance of the UNet across different datasets.
Category: Generic
Lymphoma is cancer originated in the lymphatic system. Clinically, automatic and accurate lymphoma segmentation is critical yet challenging. Recently, UNet-like architectures are widely used for medical image segmentation. The pure UNet-like architectures can model the spatial correlation between the feature maps very well, whereas they discard the critical temporal correlation. Some prior works combine UNet with recurrent neural networks (RNNs) to utilize the spatial and temporal correlation simultaneously. However, it is inconvenient to incorporate some advanced techniques proposed for UNet to RNNs, which hampers their further improvements. In this paper, we propose a recurrent dense siamese decoder architecture, which simulates RNNs and can densely utilize the spatial temporal correlation between the decoder feature maps following a "UNet" approach. We combine it with a modified hyper dense encoder. Therefore, the proposed model is a UNet with a hyper dense encoder and a recurrent dense siamese decoder (HD-RDS-UNet). To stabilize the training process, we propose a weighted Dice loss with stable gradient and self-adaptive parameters. We perform patient-independent five-fold cross-validation on 3D volumes collected from whole-body PET/CT scans of patients with lymphomas. The experimental results show that the volume-wise average Dice score and sensitivity are 85.58% and 94.63%, respectively. The patient-wise average Dice score and sensitivity are 85.85% and 95.01%, respectively. The different configurations of HD-RDS-UNet consistently show superiority in the performance comparison. Besides, a trained HD-RDS-UNet can be easily pruned, resulting in significantly reduced inference time and memory usage, while keeping very good segmentation performance.
Category: Generic
Medical image segmentation is a crucial step in developing medical systems, especially for assisting doctors in diagnosing and treating diseases. Currently, UNet has become the preferred network for most medical image segmentation tasks and has achieved tremendous success. However, due to the limitations of convolutional operation mechanisms, its ability to model long-range dependencies between features is limited. With the success of transformers in the computer vision (CV) field, many excellent models that combine transformers with UNet have emerged, but most of them have fixed receptive fields and a single feature extraction method. To address this issue, we propose a transformer-CNN interactive (TCI) feature extraction module and use it to construct TCI-UNet. Specifically, we improve the self-attention mechanism in transformers to enhance the guiding ability of attention maps for computational resource allocation. It can strengthen the network's ability to capture global contextual information from feature maps. Additionally, we introduce local multi-scale information to supplement feature information, allowing the network to focus on important local information while modeling global contextual information. This improves the network's capability to extract feature map information and facilitates effective interaction between global and local information within the transformer, enhancing the representational power of transformers. We conducted a large number of experiments on the LiTS-2017 and ISIC-2018 datasets to verify the effectiveness of our proposed method, with DCIE values of 93.81% and 88.22%, respectively. Through ablation experiments, we proved the effectiveness of the TCI module, and in comparison with other state-of-the-art (SOTA) networks, we demonstrated the superiority of TCI-UNet in accuracy and generalization.
Category: Generic
<i>Objective</i>. Glioma is one of the most fatal cancers in the world which has been divided into low grade glioma (LGG) and high grade glioma (HGG), and its image grading has become a hot topic of contemporary research. Magnetic resonance imaging (MRI) is a vital diagnostic tool for brain tumor detection, analysis, and surgical planning. Accurate and automatic glioma grading is crucial for speeding up diagnosis and treatment planning. Aiming at the problems of (1) large number of parameters, (2) complex calculation, and (3) poor speed of the current glioma grading algorithms based on deep learning, this paper proposes a lightweight 3D UNet deep learning framework, which can improve classification accuracy in comparison with the existing methods.<i>Approach</i>. To improve efficiency while maintaining accuracy, existing 3D UNet has been excluded, and depthwise separable convolution has been applied to 3D convolution to reduce the number of network parameters. The weight of parameters on the basis of space and channel compression & excitation module has been strengthened to improve the model in the feature map, reduce the weight of redundant parameters, and strengthen the performance of the model.<i>Main results</i>. A total of 560 patients with glioma were retrospectively reviewed. All patients underwent MRI before surgery. The experiments were carried out on T1w, T2w, fluid attenuated inversion recovery, and CET1w images. Additionally, a way of marking tumor area by cube bounding box is presented which has no significant difference in model performance with the manually drawn ground truth. Evaluated on test datasets using the proposed model has shown good results (with accuracy of 89.29%).<i>Significance</i>. This work serves to achieve LGG/HGG grading by simple, effective, and non-invasive diagnostic approaches to provide diagnostic suggestions for clinical usage, thereby facilitating hasten treatment decisions.
Category: Generic
In recent years, an increasing number of people have myopia in China, especially the younger generation. Common myopia may develop into high myopia. High myopia causes visual impairment and blindness. Parapapillary atrophy (PPA) is a typical retinal pathology related to high myopia, which is also a basic clue for diagnosing high myopia. Therefore, accurate segmentation of the PPA is essential for high myopia diagnosis and treatment. In this study, we propose an optimized Unet (OT-Unet) to solve this important task. OT-Unet uses one of the pre-trained models: Visual Geometry Group (VGG), ResNet, and Res2Net, as a backbone and is combined with edge attention, parallel partial decoder, and reverse attention modules to improve the segmentation accuracy. In general, using the pre-trained models can improve the accuracy with fewer samples. The edge attention module extracts contour information, the parallel partial decoder module combines the multi-scale features, and the reverse attention module integrates high- and low-level features. We also propose an augmented loss function to increase the weight of complex pixels to enable the network to segment more complex lesion areas. Based on a dataset containing 360 images (Including 26 pictures provided by PALM), the proposed OT-Unet achieves a high AUC (Area Under Curve) of 0.9235, indicating a significant improvement over the original Unet (0.7917).
Category: Generic
Automatic vessel segmentation is a key step of clinical or pre-clinical vessel bio-markers for clinical diagnosis. In previous research, the segmentation architectures are mainly based on Convolutional Neural Networks (CNN). However, due to the limitation of the receipt of field (ROF) of convolution operation, it is difficult to further improve the accuracy of the CNN-based methods. To solve this problem, a Squeeze-Excitation Transformer U-net (SETUnet) is proposed to break the ROF limitation of CNN. The proposed squeeze-excitation Transformer can introduce the self attention mechanism into the vessel segmentation task by generating a global attention mapping according to the entire vessel image. To test the performance of the proposed SETUnet, the SETUnet is trained and tested on several public vessel data-sets. The results show that the SETUnet outperforms several state-of-the-art vessel segmentation neural networks, especially on the connectivity of the segmented vessels.
Category: Generic
Convolutional Neural Network (CNN) plays a vital role in the development of computer vision applications. The depth neural network composed of U-shaped structures and jump connections is widely used in various medical image tasks. Recently, based on the self-attention mechanism, the Transformer structure has made great progress and tends to replace CNN, and it has great advantages in understanding global information. In this paper, the ConvWin Transformer structure is proposed, which refers to the W-MSA structure in Swin and combines with the convolution. It can not only accelerate the convergence speed, but also enrich the information exchange between patches and improve the understanding of local information. Then, it is integrated with UNet, a U-shaped architecture commonly used in medical image segmentation, to form a structure called ConvWin-UNet. Meanwhile, this paper improves the patch expanding layer to perform the upsampling operation. The experimental results on the Hubmap datasets and synapse multi-organ segmentation dataset indicate that the proposed ConvWin-UNet structure achieves excellent results. Partial code and models of this work are available at https://github.com/xmFeng-hdu/ConvWin-UNet.
Category: Generic
Accurate segmentation of frontal lobe areas on magnetic resonance imaging (MRI) can assist in diagnosing and managing idiopathic normal-pressure hydrocephalus. However, frontal lobe segmentation is challenging due to the complexity of the degree and shape of damage and the ambiguity of the boundaries of frontal lobe sites. Therefore, to extract the rich edge information and feature representation of the frontal lobe, this paper designs an edge guidance (EG) module to enhance the representation of edge features. Accordingly, an edge-guided cascade network framework (EG-Net) is proposed to segment frontal lobe parts automatically. Two-dimensional MRI slice images are fed into the edge generation and segmentation networks. First, the edge generation network extracts the edge information from the input image. Then, the edge information is sent to the EG module to generate an edge attention map for feature representation enhancement. Meanwhile, multi-scale attentional convolution (MSA) is utilized in the feature coding stage of the segmentation network to obtain feature responses from different perceptual fields in the coding stage and enrich the spatial context information. Besides, the feature fusion module is employed to selectively aggregate the multi-scale features in the coding stage with the edge features output by the EG module. Finally, the two components are fused, and a decoder recovers the spatial information to generate the final prediction results. An extensive quantitative comparison is performed on a publicly available brain MRI dataset (MICCAI 2012) to evaluate the effectiveness of the proposed algorithm. The experimental results indicate that the proposed method achieves an average DICE score of 95.77% compared to some advanced methods, which is 4.96% better than the classical U-Net. The results demonstrate the potential of the proposed EG-Net in improving the accuracy of frontal edge pixel classification through edge guidance.
Category: Generic
Orthodontic treatment profoundly impact the pharyngeal airway (PA) of patients. Airway examination is an integral part of daily orthodontic diagnosis, and lateral cephalograms (LC) are reliable to reveal PA structures. This study attempted to develop a simple method to help clinicians make a preliminary judgement of patients' PA conditions and assess the impact of orthodontic treatment on their airways.