Brain tumor is deliberated as one of the severe health complications which lead to decrease in life expectancy of the individuals and is also considered as a prominent cause of mortality worldwide. Therefore, timely detection and prediction of brain tumors can be helpful to prevent death rates due to brain tumors. Biomedical image analysis is a widely known solution to diagnose brain tumor. Although MRI is the current standard method for imaging tumors, its clinical usefulness is constrained by the requirement of manual segmentation which is time-consuming. Deep learning-based approaches have emerged as a promising solution to develop automated biomedical image exploration tools and the UNet architecture is commonly used for segmentation. However, the traditional UNet has limitations in terms of complexity, training, accuracy, and contextual information processing. As a result, the modified UNet architecture, which incorporates residual dense blocks, layered attention, and channel attention modules, in addition to stacked convolution, can effectively capture both coarse and fine feature information. The proposed SLCA UNet approach achieves good performance on the freely accessible Brain Tumor Segmentation (BraTS) dataset, with an average performance of 0.845, 0.845, 0.999, and 8.1 in terms of Dice, Sensitivity, Specificity, and Hausdorff95 for BraTS 2020 dataset, respectively.
Huazhong University of Science and Technology（华中科技大学）；Shenzhen University（深圳大学）
Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contextual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose…
Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation
School of Electronics Engineering, Vellore Institute of Technology（韦洛尔理工大学电子工程学院）；Centre for Advanced Data Science, Vellore Institute of Technology（韦洛尔理工大学高级数据科学中心）
Ashford and St Peter’s Hospitals NHS Foundation Trust（阿什福德和圣彼得医院 NHS 基金会信托）；University of Lincoln（林肯大学）
In this paper we propose a 2D deep residual Unet with 104 convolutional layers (DR-Unet104) for lesion segmentation in brain MRIs. We make multiple additions to the Unet architecture, including adding the 'bottleneck' residual block to the Unet encoder and adding dropout after each convolution block stack. We verified the effect of introducing the regularisation of dropout with small rate (e.g. 0.2) on the architecture, and found a dropout of 0.2 improved the overall performance compared to no dropout, or a dropout of 0.5. We evaluated the proposed architecture as part of the Multimodal Brain …
Convolutional neural networks (CNNs) have achieved remarkable success in automatically segmenting organs or lesions on 3D medical images. Recently, vision transformer networks have exhibited exceptional performance in 2D image classification tasks. Compared with CNNs, transformer networks have an appealing advantage of extracting long-range features due to their self-attention algorithm. Therefore, we propose a CNN-Transformer combined model, called BiTr-Unet, with specific modifications for brain tumor segmentation on multi-modal MRI scans. Our BiTr-Unet achieves good performance on the BraTS…
Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, 3D networks have some drawbacks due to their high cost on computational resources. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, named RA-UNet, to precisely extract the liver volume of interests (VOI) and segment tumors from the l…
Faculty of Computer Science, AGH University of Krakow（计算机科学系，克拉科夫AGH大学）；Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta（信息系，全国 veterans 大学 Yogya 市）
Background and objective: Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Preoperative tumor localization, differential diagnosis, and subsequent selection of appropriate treatment for parotid gland tumors are critical. However, the relative rarity of these tumors and the highly dispersed tissue types have left an unmet need for a subtle differential diagnosis of such neoplastic lesions based on preoperative radiomics. Recently, deep learning methods have developed rapidly, especially Transformer beats the traditional convolutional neural network in computer v…
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation
University of Engineering and Technology, Lahore（拉合尔工程技术大学）；School of Interdisciplinary Engineering and Sciences, National University of Sciences and Technology（国家科技大学跨学科工程与科学学院）
Universitat Politècnica de Catalunya. BarcelonaTech（加泰罗尼亚理工大学·巴塞罗那理工）
Automation of brain tumor segmentation in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is especially critical in medical diagnosis. This work studies 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and decrease the effect of unbalanced data. The different trained model…
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNet
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital & Institute, Sichuan Cancer Center（四川省肿瘤医院·研究所（四川省癌症防治中心））
An Ensemble Approach for Brain Tumor Segmentation and Synthesis
Paul G. Allen School of Computer Science, University of Washington（华盛顿大学保罗G.艾伦计算机科学学院）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Department of Bioengineering, University of Washington（华盛顿大学生物工程系）；Department of Electrical and Computer Engineering, University of Washington（华盛顿大学电气与计算机工程系）；Department of Mechanical Engineering, University of Washington（华盛顿大学机械工程系）；Watauga High School（沃托加高中）；Department of Neurological Surgery, University of Washington（华盛顿大学神经外科系）
Comparative Study of UNet-based Architectures for Liver Tumor Segmentation in Multi-Phase Contrast-Enhanced Computed Tomography
School of Computer Science and Engineering（计算机科学与工程学院）；The Saigon International University（暹罗国际大学）；School of Biomedical Engineering（生物医学工程学院）；International University（国际大学）；Vietnam National University HCMC（越南国家大学胡志明市分校）
Deep learning algorithms are preferable for rectal tumor segmentation. However, it is still a challenge task to accurately segment and identify the locations and sizes of rectal tumors by using deep learning methods. To increase the capability of extracting enough feature information for rectal tumor segmentation, we propose a Covariance Self-Attention Dual Path UNet (CSA-DPUNet). The proposed network mainly includes two improvements on UNet: 1) modify UNet that has only one path structure to consist of two contracting path and two expansive paths (nam new network as DPUNet), which can help ex…
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention
Department of Medical Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS)（医学系，巴基斯坦工程与应用科学研究所）；Pakistan Institute of Engineering and Applied Sciences (PIEAS)（巴基斯坦工程与应用科学研究所）；Department of Physics & Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences (PIEAS)（物理与应用数学系，巴基斯坦工程与应用科学研究所）；Center for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS)（数学科学中心，巴基斯坦工程与应用科学研究所）；Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences（模式识别实验室，计算机与信息科学系，巴基斯坦工程与应用科学研究所）；Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学研究所）；Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences（人工智能中心（PAIC），巴基斯坦工程与应用科学研究所）
Vision Transformers (ViT)s have recently become popular due to their outstanding modeling capabilities, in particular for capturing long-range information, and scalability to dataset and model sizes which has led to state-of-the-art performance in various computer vision and medical image analysis tasks. In this work, we introduce a unified framework consisting of two architectures, dubbed UNetFormer, with a 3D Swin Transformer-based encoder and Convolutional Neural Network (CNN) and transformer-based decoders. In the proposed model, the encoder is linked to the decoder via skip connections at…
Shenzhen University（深圳大学）；Huazhong University of Science and Technology（华中科技大学）
The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional neural networks (CNNs), mainly fully convolutional networks, have shown promising brain tumor segmentation. This paper further boosts the performance…
Shanghai University（上海大学）；Shanghai Jiao Tong University（上海交通大学）；University of Chinese Academy of Sciences（中国科学院大学）；Northeastern University（东北大学）
Liver tumor segmentation in CT images is a critical step in the diagnosis, surgical planning and postoperative evaluation of liver disease. An automatic liver and tumor segmentation method can greatly relieve physicians of the heavy workload of examining CT images and better improve the accuracy of diagnosis. In the last few decades, many modifications based on U-Net model have been proposed in the literature. However, there are relatively few improvements for the advanced UNet++ model. In our paper, we propose an encoding feature supervised UNet++(ES-UNet++) and apply it to the liver and tumo…
Dino-NestedUNet: Unlocking Foundation Vision Encoders for Pathology Tumor Bulk Segmentation via Dense Decoding
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA（病理学系，医学学院，俄亥俄州立大学韦克斯纳医学中心，哥伦布，OH，USA）；Department of Biomedical Engineering, The Ohio State University, Columbus, OH, USA（生物医学工程系，俄亥俄州立大学，哥伦布，OH，USA）
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Brain tumor segmentation is essential for the diagnosis and prognosis of patients with gliomas. The brain tumor segmentation challenge has continued to provide a great source of data to develop automatic algorithms to perform the task. This paper describes our contribution to the 2021 competition. We developed our methods based on nn-UNet, the winning entry of last year competition. We experimented with several modifications, including using a larger network, replacing batch normalization with group normalization, and utilizing axial attention in the decoder. Internal 5-fold cross validation a…
NUS Graduate School for Integrative Sciences and Engineering, NUS（新加坡国立大学综合科学与工程研究生院）；National University of Singapore（新加坡国立大学）；NIT, Tiruchirappalli（蒂鲁吉拉伯利国立理工学院）；University of Moratuwa（莫拉图瓦大学）；Punjab Engineering College（旁遮普工程学院）
In this work, we develop an attention convolutional neural network (CNN) to segment brain tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using various machine learning methods. We adopt a 3D UNet architecture and integrate channel and spatial attention with the decoder network to perform segmentation. For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient. We also perform extensive experiments t…
University Hospital Essen（埃森大学医院）；Cancer Research Center Cologne Essen（科隆埃森癌症研究中心）；University Medicine Essen（埃森大学医学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；University of Minho（米尼奥大学）；Computer Algorithms for Medicine Laboratory（医学计算机算法实验室）
Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy. In this context, FDG-PET/CT scans are routinely used for staging and re-staging of cancer, as the radiolabeled fluorodeoxyglucose is taken up in regions of high metabolism. Unfortunately, these regions with high metabolism are not specific to tumors and can also represent physiological uptake by normal functioning organs, inflammation, or infection, making detailed and reliable tumor segmentation in these scans a demanding task. This gap in research is addressed by the AutoPET challenge, wh…
Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer
University of Information Technology（信息科技大学）；Vietnam National University（越南国家大学）；College of Technology and Design, University of Economics Ho Chi Minh City（胡志明市经济大学技术与设计学院）；Ho Chi Minh City Open University（胡志明市开放大学）
Gliomas are the most common and aggressive among brain tumors, which cause a short life expectancy in their highest grade. Therefore, treatment assessment is a key stage to enhance the quality of the patients' lives. Recently, deep convolutional neural networks (DCNNs) have achieved a remarkable performance in brain tumor segmentation, but this task is still difficult owing to high varying intensity and appearance of gliomas. Most of the existing methods, especially UNet-based networks, integrate low-level and high-level features in a naive way, which may result in confusion for the model. Mor…
Southeast University（东南大学）；Swinburne University of Technology（斯威本科技大学）；Communication University of China（中国传媒大学）
Precise segmentation of organs and tumors plays a crucial role in clinical applications. It is a challenging task due to the irregular shapes and various sizes of organs and tumors as well as the significant class imbalance between the anatomy of interest (AOI) and the background region. In addition, in most situation tumors and normal organs often overlap in medical images, but current approaches fail to delineate both tumors and organs accurately. To tackle such challenges, we propose a position-guided deformable UNet, namely PGD-UNet, which exploits the spatial deformation capabilities of d…
Leiden University Medical Center（莱顿大学医学中心）；Sharif University of Technology（谢里夫理工大学）；Netherlands Cancer Institute（荷兰癌症研究所）；Delft University of Technology（代尔夫特理工大学）
Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim t…
Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation. The popular "U-shaped" network architecture has achieved state-of-the-art performance benchmarks on different 2D and 3D semantic segmentation tasks and across various imaging modalities. However, due to the limited…
Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmentation approach that augments training data using pseudo MR images produced by transforming expert-segmented CT images was developed. Eighty-One T2-weighted MRI scans from 28 patients with non-small cell lung cancers were analyzed. Cross-modality prior encoding the transformation of CT to pseudo MR images resembling T2w MRI was learned as a generative adversarial deep learning model. This model augmented training data arising from 6 expert-segmented …
Instead of using current deep-learning segmentation models (like the UNet and variants), we approach the segmentation problem using trained Convolutional Neural Network (CNN) classifiers, which automatically extract important features from images for classification. Those extracted features can be visualized and formed into heatmaps using Gradient-weighted Class Activation Mapping (Grad-CAM). This study tested whether the heatmaps could be used to segment the classified targets. We also proposed an evaluation method for the heatmaps; that is, to re-train the CNN classifier using images filtere…
Tianjin University（天津大学）；Institute of High Performance Computing, A*STAR（A*STAR高性能计算研究所）；Imperial College London（帝国理工学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
In recent years, Denoising Diffusion Models have demonstrated remarkable success in generating semantically valuable pixel-wise representations for image generative modeling. In this study, we propose a novel end-to-end framework, called Diff-UNet, for medical volumetric segmentation. Our approach integrates the diffusion model into a standard U-shaped architecture to extract semantic information from the input volume effectively, resulting in excellent pixel-level representations for medical volumetric segmentation. To enhance the robustness of the diffusion model's prediction results, we als…
Frequency-Aware Ensemble Learning for BraTS 2025 Pediatric Brain Tumor Segmentation
Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC, Shanghai Jiao Tong University（自然科学院、数学科学学院、MOE-LSC、上海交通大学）；Department of Radiology, Huashan Hospital, Fudan University（放射科、华山医院、复旦大学）；Department of Neurosurgery, Huashan Hospital, Fudan University（神经外科、华山医院、复旦大学）
Otto-von-Guericke University（奥托·冯·格里克大学）；University Hospital of Magdeburg（马格德堡大学医院）
Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. T…
SPEEDNet: Salient Pyramidal Enhancement Encoder-Decoder Network for Colonoscopy Images
Indian Institute of Information Technology (IIIT) Jabalpur（印度贾巴尔普尔信息技术学院）；Gujarat Technological University（古吉拉特理工大学）；Indian Institute of Technology (IIT) Roorkee（印度理工学院鲁尔基分校）；SVIMS Tirupati（蒂鲁帕蒂斯里文卡特斯瓦拉医学科学研究所）
Macquarie University（麦考瑞大学）；University of Technology Sydney（悉尼科技大学）；The University of Sydney（悉尼大学）
Automatic tumor segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because regular CNNs can only exploit translation invariance, ignoring further inherent symmetries existing in medical images such as rotations and reflections. To mitigate this shortcoming, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning mor…
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
University of Washington（华盛顿大学）；Paul G. Allen School of Computer Science, University of Washington（华盛顿大学保罗·G·艾伦计算机科学学院）；Department of Neurological Surgery, University of Washington（华盛顿大学神经外科（注：此处按要求提取到学院/大学级别，实际原文该机构属于华盛顿大学，故提取为华盛顿大学））
Indian Institute of Technology Jodhpur（印度理工学院焦特布尔分校）；Shri Guruji Gobind Singhji Institute of Engineering and Technology（什里古鲁吉·戈宾德·辛格吉工程技术学院）
Colorectal cancer is a leading cause of death worldwide. However, early diagnosis dramatically increases the chances of survival, for which it is crucial to identify the tumor in the body. Since its imaging uses high-resolution techniques, annotating the tumor is time-consuming and requires particular expertise. Lately, methods built upon Convolutional Neural Networks(CNNs) have proven to be at par, if not better in many biomedical segmentation tasks. For the task at hand, we propose another CNN-based approach, which uses atrous convolutions and residual connections besides the conventional fi…
As an essential prerequisite for developing a medical intelligent assistant system, medical image segmentation has received extensive research and concentration from the neural network community. A series of UNet-like networks with encoder-decoder architecture has achieved extraordinary success, in which UNet2+ and UNet3+ redesign skip connections, respectively proposing dense skip connection and full-scale skip connection and dramatically improving compared with UNet in medical image segmentation. However, UNet2+ lacks sufficient information explored from the full scale, which will affect the…
