EliasHossain/CancerAbstracts huggingface dataset:
Category: Generic
Medical image segmentation plays a vital role in computer-aided diagnosis procedures. Recently, U-Net is widely used in medical image segmentation. Many variants of U-Net have been proposed, which attempt to improve the network performance while keeping the U-shaped structure unchanged. However, this U-shaped structure is not necessarily optimal. In this article, the effects of different parts of the U-Net on the segmentation ability are experimentally analyzed. Then a more efficient architecture, Half-UNet, is proposed. The proposed architecture is essentially an encoder-decoder network based on the U-Net structure, in which both the encoder and decoder are simplified. The re-designed architecture takes advantage of the unification of channel numbers, full-scale feature fusion, and Ghost modules. We compared Half-UNet with U-Net and its variants across multiple medical image segmentation tasks: mammography segmentation, lung nodule segmentation in the CT images, and left ventricular MRI image segmentation. Experiments demonstrate that Half-UNet has similar segmentation accuracy compared U-Net and its variants, while the parameters and floating-point operations are reduced by 98.6 and 81.8%, respectively, compared with U-Net.
Category: Generic
With the development of deep learning, medical image segmentation technology has made significant progress in the field of computer vision. The Unet is a pioneering work, and many researchers have conducted further research based on this architecture. However, we found that most of these architectures are improvements in the backward propagation and integration of the network, and few changes are made to the forward propagation and information integration of the network. Therefore, we propose a feedback mechanism Unet (FM-Unet) model, which adds feedback paths to the encoder and decoder paths of the network, respectively, to help the network fuse the information of the next step in the current encoder and decoder. The problem of encoder information loss and decoder information shortage can be well solved. The proposed model has more moderate network parameters, and the simultaneous multi-node information fusion can alleviate the gradient disappearance. We have conducted experiments on two public datasets, and the results show that FM-Unet achieves satisfactory results.
Category: Generic
Roads are important mode of transportation, which are very convenient for people's daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a complement UNet (C-UNet). C-UNet contains four modules. Firstly, the standard UNet is used to roughly extract road information from remote sensing images, getting the first segmentation result; secondly, a fixed threshold is utilized to erase partial extracted information; thirdly, a multi-scale dense dilated convolution UNet (MD-UNet) is introduced to discover the complement road areas in the erased masks, obtaining the second segmentation result; and, finally, we fuse the extraction results of the first and the third modules, getting the final segmentation results. Experimental results on the Massachusetts Road dataset indicate that our C-UNet gets the higher results than the state-of-the-art methods, demonstrating its effectiveness.
Category: Generic
Quantitative photoacoustic tomography (QPAT) is a valuable tool in characterizing ovarian lesions for accurate diagnosis. However, accurately reconstructing a lesion's optical absorption distributions from photoacoustic signals measured with multiple wavelengths is challenging because it involves an ill-posed inverse problem with three unknowns: the Gr neisen parameter <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo>(</mo> <mi> </mi> <mo>)</mo></mrow> </math> , the absorption distribution, and the optical fluence <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo>(</mo> <mi> </mi> <mo>)</mo></mrow> </math> . Here, we propose a novel ultrasound-enhanced Unet model (US-Unet) that reconstructs optical absorption distribution from PAT data. A pre-trained ResNet-18 extracts the US features typically identified as morphologies of suspicious ovarian lesions, and a Unet is implemented to reconstruct optical absorption coefficient maps, using the initial pressure and US features extracted by ResNet-18. To test this US-Unet model, we calculated the blood oxygenation saturation values and total hemoglobin concentrations from 655 regions of interest (ROIs) (421 benign, 200 malignant, and 34 borderline ROIs) obtained from clinical images of 35 patients with ovarian/adnexal lesions. A logistic regression model was used to compute the ROC, the area under the ROC curve (AUC) was 0.94, and the accuracy was 0.89. To the best of our knowledge, this is the first study to reconstruct quantitative PAT with PA signals and US-based structural features.
Category: Generic
Chest CT scan is an effective way to detect and diagnose COVID-19 infection. However, features of COVID-19 infection in chest CT images are very complex and heterogeneous, which make segmentation of COVID-19 lesions from CT images quite challenging.
Category: Generic
This paper discusses a semantic segmentation framework and shows its application in agricultural intelligence, such as providing environmental awareness for agricultural robots to work autonomously and efficiently. We propose an ensemble framework based on the bagging strategy and the UNet network, using RGB and HSV color spaces. We evaluated the framework on our self-built dataset (Maize) and a public dataset (Sugar Beets). Then, we compared it with UNet-based methods (single RGB and single HSV), DeepLab V3+, and SegNet. Experimental results show that our ensemble framework can synthesize the advantages of each color space and obtain the best IoUs (0.8276 and 0.6972) on the datasets (Maize and Sugar Beets), respectively. In addition, including our framework, the UNet-based methods have faster speed and a smaller parameter space than DeepLab V3+ and SegNet, which are more suitable for deployment in resource-constrained environments such as mobile robots.
Category: Generic
High precision segmentation of retinal blood vessels from retinal images is a significant step for doctors to diagnose many diseases such as glaucoma and cardiovascular diseases. However, at the peripheral region of vessels, previous U-Net-based segmentation methods failed to significantly preserve the low-contrast tiny vessels.
Category: Generic
Cervical cancer diagnosis hinges significantly on precise nuclei segmentation at early stages, which however, remains largely elusive due to challenges such as overlapping cells and blurred nuclei boundaries. This paper presents a novel deep neural network (DNN), the Global Context UNet (GC-UNet), designed to adeptly handle intricate environments and deliver accurate cell segmentation. At the core of GC-UNet is DenseNet, which serves as the backbone, encoding cell images and capitalizing on pre-existing knowledge. A unique context-aware pooling module, equipped with a gating model, is integrated for effective encoding of ImageNet pre-trained features, ensuring essential features at different levels are retained. Further, a decoder grounded in a global context attention block is employed to foster global feature interaction and refine the predicted masks.
Category: Generic
In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different imaging systems to obtain this information. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The semantic segmentation aims to divide the images into regions with comparable characteristics, including intensity, homogeneity, and texture. UNET is the deep learning network that segments the critical features. However, UNETs basic architecture cannot accurately segment complex MRI images. This review introduces the modified and improved models of UNET suitable for increasing segmentation accuracy.
Category: Generic
Precise segmentation is in demand for hepatocellular carcinoma or metastasis clinical diagnosis due to the heterogeneous appearance and diverse anatomy of the liver on scanned abdominal computed tomography (CT) images. In this study, we present an automatic unified registration-free deep-learning-based model with residual block and dilated convolution for training end-to-end liver and lesion segmentation. A multi-scale approach has also been utilized to explore novel inter-slice features with multi-channel input images. A novel objective function is introduced to deal with fore- and background pixels imbalance based on the joint metric of dice coefficient and absolute volumetric difference. Further, batch normalization is used to improve the learning without any loss of useful information. The proposed methodology is extensively validated and tested on 30% of the publicly available Dircadb, LiTS, Sliver07, and Chaos datasets. A comparative analysis is conducted based on multiple evaluation metrics frequently used in segmentation competitions. The results show substantial improvement, with mean dice scores of 97.31, 97.38, 97.39 and 95.49% for the Dircadb, LiTS, Sliver07, and Chaos liver test sets, and 91.92 and 86.70% for Dircadb and LiTS lesion segmentation. It should be noted that we achieve the best lesion segmentation performance on common datasets. The obtained qualitative and quantitative results demonstrate that our proposed model outperform other state-of-the-art methods for liver and lesion segmentation, with competitive performance on additional datasets. Henceforth, it is envisaged as being applicable to pertinent medical segmentation applications.
Category: Generic
Deep learning based medical image segmentation methods have been widely used for thyroid gland segmentation from ultrasound images, which is of great importance for the diagnosis of thyroid disease since it can provide various valuable sonography features. However, existing thyroid gland segmentation models suffer from: (1) low-level features that are significant in depicting thyroid boundaries are gradually lost during the feature encoding process, (2) contextual features reflecting the changes of difference between thyroid and other anatomies in the ultrasound diagnosis process are either omitted by 2D convolutions or weakly represented by 3D convolutions due to high redundancy. In this work, we propose a novel hybrid transformer UNet (H-TUNet) to segment thyroid glands in ultrasound sequences, which consists of two parts: (1) a 2D Transformer UNet is proposed by utilizing a designed multi-scale cross-attention transformer (MSCAT) module on every skipped connection of the UNet, so that the low-level features from different encoding layers are integrated and refined according to the high-level features in the decoding scheme, leading to better representation of differences between anatomies in one ultrasound frame; (2) a 3D Transformer UNet is proposed by applying a 3D self-attention transformer (SAT) module to the very bottom layer of 3D UNet, so that the contextual features representing visual differences between regions and consistencies within regions could be strengthened from successive frames in the video. The learning process of the H-TUNet is formulated as a unified end-to-end network, so the intra-frame feature extraction and inter-frame feature aggregation can be learned and optimized jointly. The proposed method was evaluated on Thyroid Segmentation in Ultrasonography Dataset (TSUD) and TG3k Dataset. Experimental results have demonstrated that our method outperformed other state-of-the-art methods with respect to the certain benchmarks for thyroid gland segmentation.
Category: Generic
In view of the limitations of current deep learning models in segmenting dental cone-beam computed tomography (CBCT) images, specifically dealing with complex root morphological features, fuzzy boundaries between tooth roots and alveolar bone, and the need for costly annotation of dental CBCT images. We collected dental CBCT data from 200 patients and annotated 45 of them for network training, and proposed a CNN-Transformer Architecture UNet network, which combines the advantages of CNN and Transformer. The CNN component effectively extracts local features, while the Transformer captures long-range dependencies. Multiple spatial attention modules were included to enhance the network's ability to extract and represent spatial information. Additionally, we introduced a novel Masked image modeling method to pre-train the CNN and Transformer modules simultaneously, mitigating limitations due to a smaller amount of labeled training data. Experimental results demonstrate that the proposed method achieved superior performance (DSC of 87.12%, IoU of 78.90%, HD95 of 0.525 mm, ASSD of 0.199 mm), and provides a more efficient and effective approach to automatically and accurately segment dental CBCT images, has real-world applicability in orthodontics and dental implants.
Category: Generic
Recent advances in deep learning (DL) have provided promising solutions to medical image segmentation. Among existing segmentation approaches, the U-Net-based methods have been used widely. However, very few U-Net-based studies have been conducted on automatic segmentation of the human brain claustrum (CL). The CL segmentation is challenging due to its thin, sheet-like structure, heterogeneity of its image modalities and formats, imperfect labels, and data imbalance. We propose an automatic optimized U-Net-based 3D segmentation model, called AM-UNet, designed as an end-to-end process of the pre and post-process techniques and a U-Net model for CL segmentation. It is a lightweight and scalable solution which has achieved the state-of-the-art accuracy for automatic CL segmentation on 3D magnetic resonance images (MRI). On the T1/T2 combined MRI CL dataset, AM-UNet has obtained excellent results, including Dice, Intersection over Union (IoU), and Intraclass Correlation Coefficient (ICC) scores of 82%, 70%, and 90%, respectively. We have conducted the comparative evaluation of AM-UNet with other pre-existing models for segmentation on the MRI CL dataset. As a result, medical experts confirmed the superiority of the proposed AM-UNet model for automatic CL segmentation. The source code and model of the AM-UNet project is publicly available on GitHub: https://github.com/AhmedAlbishri/AM-UNET.
Category: Generic
Treatment choices for patients with bladder cancer (BCa) are determined by the presence of muscular invasion. The precise segmentation of the inner and outer walls (IW and OW), as well as the bladder tumor (BT), is crucial for improving computer-aided diagnosis of muscle-invasive bladder cancer (MIBC).
Category: Generic
Weather nowcasting consists of predicting meteorological components in the short term at high spatial resolutions. Due to its influence in many human activities, accurate nowcasting has recently gained plenty of attention. In this paper, we treat the nowcasting problem as an image-to-image translation problem using satellite imagery. We introduce Broad-UNet, a novel architecture based on the core UNet model, to efficiently address this problem. In particular, the proposed Broad-UNet is equipped with asymmetric parallel convolutions as well as Atrous Spatial Pyramid Pooling (ASPP) module. In this way, the Broad-UNet model learns more complex patterns by combining multi-scale features while using fewer parameters than the core UNet model. The proposed model is applied on two different nowcasting tasks, i.e. precipitation maps and cloud cover nowcasting. The obtained numerical results show that the introduced Broad-UNet model performs more accurate predictions compared to the other examined architectures.
Category: Generic
To obtain a high-resolution hyperspectral image (HR-HSI), fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) is a prominent approach. Numerous approaches based on convolutional neural networks (CNNs) have been presented for hyperspectral image (HSI) and multispectral image (MSI) fusion. Nevertheless, these CNN-based methods may ignore the global relevant features from the input image due to the geometric limitations of convolutional kernels. To obtain more accurate fusion results, we provide a spatial-spectral transformer-based U-net (SSTF-Unet). Our SSTF-Unet can capture the association between distant features and explore the intrinsic information of images. More specifically, we use the spatial transformer block (SATB) and spectral transformer block (SETB) to calculate the spatial and spectral self-attention, respectively. Then, SATB and SETB are connected in parallel to form the spatial-spectral fusion block (SSFB). Inspired by the U-net architecture, we build up our SSTF-Unet through stacking several SSFBs for multiscale spatial-spectral feature fusion. Experimental results on public HSI datasets demonstrate that the designed SSTF-Unet achieves better performance than other existing HSI and MSI fusion approaches.
Category: Generic
Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation.
Category: Generic
Accurate segmentation of medical images is crucial for clinical diagnosis and evaluation. However, medical images have complex shapes, the structures of different objects are very different, and most medical datasets are small in scale, making it difficult to train effectively. These problems increase the difficulty of automatic segmentation. To further improve the segmentation performance of the model, we propose a multi-branch network model, called TransCUNet, for segmenting medical images of different modalities. The model contains three structures: cross residual fusion block (CRFB), pyramidal pooling module (PPM) and gated axial-attention, which achieve effective extraction of high-level and low-level features of images, while showing high robustness to different size segmentation objects and different scale datasets. In our experiments, we use four datasets to train, validate and test the models. The experimental results show that TransCUNet has better segmentation performance compared to the current mainstream segmentation methods, and the model has a smaller size and number of parameters, which has great potential for clinical applications.
Category: Generic
In order to assist physicians in diagnosis and treatment planning, accurate and automatic methods of organ segmentation are needed in clinical practice. UNet and its improved models, such as UNet + + and UNt3 + , have been powerful tools for medical image segmentation. In this paper, we focus on helping the encoder extract richer features and propose a N-Net for medical image segmentation. On the basis of UNet, we propose a dual encoder model to deepen the network depth and enhance the ability of feature extraction. In our implementation, the Squeeze-and-Excitation (SE) module is added to the dual encoder model to obtain channel-level global features. In addition, the introduction of full-scale skip connections promotes the integration of low-level details and high-level semantic information. The performance of our model is tested on the lung and liver datasets, and compared with UNet, UNet + + and UNet3 + in terms of quantitative evaluation with the Dice, Recall, Precision and F1 score and qualitative evaluation. Our experiments demonstrate that N-Net outperforms the work of UNet, UNet + + and UNet3 + in these three datasets. By visual comparison of the segmentation results, N-Net produces more coherent organ boundaries and finer details.
Category: Generic
Unsupervised image segmentation is a technique that divides an image into distinct regions or objects without prior labeling. This approach offers flexibility and adaptability to various types of image data. Particularly for large datasets, it eliminates the need for manual labeling, thereby it presents advantages in terms of time and labor costs. However, when applied to retinal image segmentation, challenges arise due to variations in data, presence of noise, and manual threshold adjustments, which can lead to over-segmentation or under-segmentation of small blood vessel boundaries and endpoints. In order to enhance the precision and accuracy of retinal image segmentation, we propose a novel image supervised segmentation network based on three-path Unet model.Firstly, the Haar wavelet transform is employed to extract high-frequency image information, which forms the foundation for the proposed HaarNet, a Unet-inspired architecture. Next, the HaarNet is integrated with the Unet and SegNet frameworks to develop a three-path Unet model, referred to as TP-Unet. Finally, the model is further refined into TP-Unet+AE+DSL by incorporating the advantages of auto-encoding (AE) and deep supervised learning (DSL) techniques, thereby enhancing the overall performance of the system. To evaluate the effectiveness of our proposed model, we conduct experiments using the DRIVE and CHASE public datasets. On the DRIVE dataset, our recommended model achieves a Dice coefficient of 0.8291 and a sensitivity index of 0.8184. These results significantly outperform the Unet model by [Formula: see text] and [Formula: see text], respectively. Furthermore, our model demonstrates excellent performance on the CHASE dataset, with a Dice coefficient of 0.8162, a sensitivity of 0.8242, and an accuracy of 0.9664. These metrics surpass the Unet model by [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Our proposed model provides more accurate and reliable results for retinal vessel segmentation, which holds significant potential for assisting doctors in their diagnosis.