Medical matting: Medical image segmentation with uncertainty from the matting perspective

Wang, Lin; Ye, Xiufen; Ju, Lie; He, Wanji; Zhang, Donghao; Wang, Xin; Huang, Yelin; Feng, Wei; Song, Kaimin; Ge, Zongyuan

doi:10.1016/j.compbiomed.2023.106714

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Experimental results show that MORLIPO can significantly improve the quality and stability of the image and video matting. The method can be applied to image processing in a wide variety of scenarios involving image editing [39], video conferencing [6], medical imaging [40], cloud detection [41], game production, intelligent transportation [42], and, more recently, multimodal 3D applications [43]. However, our method has limitations for multimodal image editing tasks, and the currently realized multimodal portrait matting is not ideal.…”

Section: Discussionmentioning

confidence: 99%

Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds

Li,

Zhang,

Geng

et al. 2024

Applied Sciences

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With the continuous development of virtual reality, digital image applications, the required complex scene video proliferates. For this reason, portrait matting has become a popular topic. In this paper, a new matting algorithm with improved portrait details for images with complex backgrounds (MORLIPO) is proposed. This work combines the background restoration module (BRM) and the fine-grained matting module (FGMatting) to achieve high-detail matting for images with complex backgrounds. We recover the background by inputting a single image or video, which serves as a priori and aids in generating a more accurate alpha matte. The main framework uses the image matting model MODNet, the MobileNetV2 lightweight network, and the background restoration module, which can both preserve the background information of the current image and provide a more accurate prediction of the alpha matte of the current frame for the video image. It also provides the background prior of the previous frame to predict the alpha matte of the current frame more accurately. The fine-grained matting module is designed to extract fine-grained details of the foreground and retain the features, while combining with the semantic module to achieve more accurate matting. Our design allows training on a single NVIDIA 3090 GPU in an end-to-end manner and experiments on publicly available data sets. Experimental validation shows that our method performs well on both visual effects and objective evaluation metrics.

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Section: Discussionmentioning

confidence: 99%

Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds

Li,

Zhang,

Geng

et al. 2024

Applied Sciences

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“…Automatic ROI segmentation and morphometric quantification of gray matter volume in MRI images decrease human biases and help to evaluate different groups in comparative or longitudinal studies ( Fornito et al, 2017 ; Nemoto et al, 2020 ). While traditional image processing techniques such as thresholding-based segmentation, watershed labeling, neuroanatomical-atlas-based segmentation, or semi-manual masking [using tools like FreeSurfer ( Fischl, 2012 ) or BET ( Smith, 2002 ) are available, the medical context often requires greater accuracy even on images with unclear borders or blurred definition ( Wang et al, 2023 )]. In this context, several machine learning techniques have been successfully used in analysis of complex datasets, including k-means clustering, Support Vector Machines (SVM), Random Forest, Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost) and Deep Learning strategies like Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Recurrent Neural Networks (RNN) ( Wang et al, 2014 ; Zhang Z. et al, 2021 ; Verma et al, 2023 ).…”

Section: Image Processing: Quantifying Connectivitymentioning

confidence: 99%

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

Caznok Silveira,

Antunes,

Athié

et al. 2024

Front. Neurosci.

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The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

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“…Medical Imaging: In the field of medical imaging, image matting technology has found applications in the segmentation and analysis of medical images. For example, Wang et al [69] introduced alpha mattes of image matting into medical imaging, which better described lesion details and addressed the blurring problem associated with binary masks. Moreover, image matting can also be utilized for medical image segmentation and disease detection [70,71]; the former can be used to separate various structures or tissues (such as lungs and bones) in medical images, assisting in analysis and diagnosis, while the latter can detect diseased tissues in medical images and help to locate lesion locations.…”

Section: Applications Of Image Mattingmentioning

confidence: 99%

Deep Learning Methods in Image Matting: A Survey

et al. 2023

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Image matting is a fundamental technique used to extract a fine foreground image from a given image by estimating the opacity values of each pixel. It is one of the key techniques in image processing and has a wide range of applications in practical scenarios, such as in image and video editing. Deep learning has demonstrated outstanding performance in various image processing tasks, making it a popular research topic. In recent years, image matting methods based on deep learning have gained significant attention due to their superior performance. Therefore, this article presents a comprehensive overview of the deep learning-based image matting algorithms that have been proposed in recent years. This paper initially introduces frequently used datasets and their production methods, along with the basic principles of traditional image matting techniques. We then analyze deep learning-based matting algorithms in detail and introduce commonly used image matting evaluation metrics. Additionally, this paper discusses the application scenarios of image matting, conducts experiments to illustrate the limitations of current image matting methods, and outlines potential future research directions in this field. Overall, this paper can serve as a valuable reference for researchers that are interested in image matting.

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Medical matting: Medical image segmentation with uncertainty from the matting perspective

Cited by 7 publications

References 19 publications

Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds

Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

Deep Learning Methods in Image Matting: A Survey

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