Multi-Resolution Image Segmentation Based on a Cascaded U-ADenseNet for the Liver and Tumors

The liver is an irreplaceable organ in the human body, maintaining life activities and metabolism. Malignant tumors of the liver have a high mortality rate at present. Computer-aided segmentation of the liver and tumors has significant effects on clinical diagnosis and treatment. There are still many challenges in the segmentation of the liver and liver tumors simultaneously, such as, on the one hand, that convolutional kernels with fixed geometric structures do not match complex, irregularly shaped targets; on the other, pooling during convolution results in a loss of spatial contextual information of images. In this work, we designed a cascaded U-ADenseNet with coarse-to-fine processing for addressing the above issues of fully automatic segmentation. This work contributes multi-resolution input images and multi-layered channel attention combined with atrous spatial pyramid pooling densely connected in the fine segmentation. The proposed model was evaluated by a public dataset of the Liver Tumor Segmentation Challenge (LiTS). Our approach attained competitive liver and tumor segmentation scores that exceeded other methods across a wide range of metrics.

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Multiscale Road Extraction in Remote Sensing Images

Computational Intelligence and Neuroscience ◽

10.1155/2019/2373798 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Aziguli Wulamu ◽

Zuxian Shi ◽

Dezheng Zhang ◽

Zheyu He

Keyword(s):

Remote Sensing ◽

Network Architecture ◽

Semantic Segmentation ◽

Road Extraction ◽

Remote Sensing Images ◽

The Road ◽

Proposed Model ◽

Different Types ◽

Spatial Pyramid Pooling ◽

The One

Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.

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Fully Convolutional DenseNet with Attention Mechanism for Liver Lesion Segmentation in MRI Images

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3617 ◽

2021 ◽

Vol 11 (8) ◽

pp. 2231-2242

Author(s):

Fei Gao ◽

Kai Qiao ◽

Jinjin Hai ◽

Bin Yan ◽

Minghui Wu ◽

...

Keyword(s):

Liver Tumor ◽

Feature Fusion ◽

Liver Tumors ◽

Contextual Information ◽

Liver Lesion ◽

Attention Mechanism ◽

Tumor Segmentation ◽

Semantic Features ◽

Lesion Segmentation ◽

High Level

The goal of this research is to achieve accurate segmentation of liver tumors in noncontrast T2-weighted magnetic resonance imaging. As liver tumors and adjacent organs are represented by pixels of very similar gray intensity, segmentation is challenging, and the presence of different sizes of liver tumor makes segmentation more difficult. Differing from previous work to capture contextual information using multiscale feature fusion with concatenation, attention mechanism is added to our segmentation model to extract precise global contextual information for pixel labeling without requiring complex dilated convolution. This study describe a liver lesion segmentation model derived from FC-DenseNet with attention mechanism. Specifically, a global attention module (GAM) is added to up-sampling path, and high-level features are processed by the GAM to generating weighting information for guiding high resolution detail features recovery. High-level features are very effective for accurate category classification, but relatively weak at pixel classification and predicting restoration of the original resolution, so the fusion of high-level semantic features and low-level detail features can improve segmentation accuracy. A weighted focal loss function is used to solve the problem of lesion area occupying a relatively low proportion of the whole image, and to deal with the disequilibrium of foreground and background in the training liver lesion images. Experimental results show our segmentation model can automatically segment liver tumors from complete MRI images, and the addition of the GAM model can effectively improve liver tumor segmentation. Our algorithms have obvious advantages over other CNN algorithms and traditional manual methods of feature extraction.

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A Viscoelastic Model for the Dynamic Behavior of Saturated Poroelastic Soils

Journal of Applied Mechanics ◽

10.1115/1.2899417 ◽

1992 ◽

Vol 59 (1) ◽

pp. 128-135 ◽

Cited By ~ 43

Author(s):

J. P. Bardet

Keyword(s):

Elastic Moduli ◽

Viscoelastic Model ◽

Degree Of Saturation ◽

Biot Theory ◽

One Dimensional ◽

Poroelastic Materials ◽

Proposed Model ◽

Wide Range ◽

Dynamic Loadings ◽

The One

A viscoelastic model is proposed to describe the dynamic response of the saturated poroelastic materials that obey the Biot theory (1956). The viscoelastic model is defined from the velocity and attenuation of dilatational and distortional waves in poroelastic materials. Its material properties are defined in terms of the elastic moduli, porosity, specific gravity, degree of saturation, and permeability of the soils. The proposed model is tested by comparing its response with the one of poroelastic materials in the case of axial and lateral harmonic loadings of one-dimensional columns. The viscoelastic model is simpler to use than poroelastic materials but yields similar results for a wide range of soils and dynamic loadings.

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Convolutional neural network for automated mass segmentation in mammography

BMC Bioinformatics ◽

10.1186/s12859-020-3521-y ◽

2020 ◽

Vol 21 (S1) ◽

Author(s):

Dina Abdelhafiz ◽

Jinbo Bi ◽

Reda Ammar ◽

Clifford Yang ◽

Sheida Nabavi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Contextual Information ◽

Automatic Segmentation ◽

Ground Truth ◽

Test Accuracy ◽

Convolutional Network ◽

Proposed Model ◽

The Mean

Abstract Background Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features. We trained the proposed model using huge publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and a private database from the University of Connecticut Health Center (UCHC). Results We compared the performance of the proposed model with those of the state-of-the-art DL models including the fully convolutional network (FCN), SegNet, Dilated-Net, original U-Net, and Faster R-CNN models and the conventional region growing (RG) method. The proposed Vanilla U-Net model outperforms the Faster R-CNN model significantly in terms of the runtime and the Intersection over Union metric (IOU). Training with digitized film-based and fully digitized MG images, the proposed Vanilla U-Net model achieves a mean test accuracy of 92.6%. The proposed model achieves a mean Dice coefficient index (DI) of 0.951 and a mean IOU of 0.909 that show how close the output segments are to the corresponding lesions in the ground truth maps. Data augmentation has been very effective in our experiments resulting in an increase in the mean DI and the mean IOU from 0.922 to 0.951 and 0.856 to 0.909, respectively. Conclusions The proposed Vanilla U-Net based model can be used for precise segmentation of masses in MG images. This is because the segmentation process incorporates more multi-scale spatial context, and captures more local and global context to predict a precise pixel-wise segmentation map of an input full MG image. These detected maps can help radiologists in differentiating benign and malignant lesions depend on the lesion shapes. We show that using transfer learning, introducing augmentation, and modifying the architecture of the original model results in better performance in terms of the mean accuracy, the mean DI, and the mean IOU in detecting mass lesion compared to the other DL and the conventional models.

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Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation

Journal of Healthcare Engineering ◽

10.1155/2019/8415485 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 8

Author(s):

Jinjin Hai ◽

Kai Qiao ◽

Jian Chen ◽

Hongna Tan ◽

Jingbo Xu ◽

...

Keyword(s):

Breast Tumor ◽

Network Architecture ◽

Malignant Tumors ◽

Automatic Segmentation ◽

Disease Diagnosis ◽

Image Features ◽

Tumor Segmentation ◽

Convolutional Network ◽

Qualitative And Quantitative ◽

Multiple Sampling

Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolutional network to achieve automatic segmentation of breast tumor in an end-to-end manner. Considering the diversity of shape and size for malignant tumors in the digital mammograms, we introduce multiscale image information into the fully convolutional dense network architecture to improve the segmentation precision. Multiple sampling rates of atrous convolution are concatenated to acquire different field-of-views of image features without adding additional number of parameters to avoid over fitting. Weighted loss function is also employed during training according to the proportion of the tumor pixels in the entire image, in order to weaken unbalanced classes problem. Qualitative and quantitative comparisons demonstrate that the proposed algorithm can achieve automatic tumor segmentation and has high segmentation precision for various size and shapes of tumor images without preprocessing and postprocessing.

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P-LINKNET: LINKNET WITH SPATIAL PYRAMID POOLING FOR HIGH-RESOLUTION SATELLITE IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-35-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 35-40

Author(s):

Y. Ding ◽

M. Wu ◽

Y. Xu ◽

S. Duan

Keyword(s):

High Resolution ◽

Contextual Information ◽

City Management ◽

Multi Scale ◽

Information Updating ◽

Proposed Model ◽

Spatial Pyramid Pooling ◽

High Resolution Satellite Imagery ◽

Deep Learning Model ◽

Spatial Pyramid

Abstract. Automatic extraction of buildings from high-resolution remote sensing imagery is very useful in many applications such as city management, mapping, urban planning and geographic information updating. Although extensively studied in the past years, due to the general texture of the building and the complexity of the image background, high-precision building segmentation from high-resolution sensing image is still a challenging task. Repeated pooling and striding operations used in CNNs reduce feature resolutions and cause the loss of detail information. In order to solve this problem, we proposed a deep learning model with a spatial pyramid pooling module based on the LinkNet. The proposed model called P-LinkNet that takes advantage of a spatial pyramid pooling module to capture and aggregate multi-scale contextual information. We tested it on Inria Building dataset. Experimental results show that the proposed P-LinkNet is superior to the LinkNet.

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An entropy based multi-thresholding method for semi-automatic segmentation of liver tumors

10.54294/xd06v0 ◽

2008 ◽

Author(s):

Anirudh Choudhary ◽

Nicola Moretto ◽

Francesca Pizzorni Ferrarese ◽

Giulia A. Zamboni

Keyword(s):

Liver Cancer ◽

Liver Tumor ◽

Liver Tumors ◽

Automatic Segmentation ◽

Single Step ◽

Tumor Segmentation ◽

Common Cause ◽

The Third ◽

Precise Analysis ◽

Liver Tumor Segmentation

Liver cancer is the fifth most commonly diagnosed cancer and the third most common cause of death from cancer worldwide. A precise analysis of the lesions would help in the staging of the tumor and in the evaluation of the possible applicable therapies. In this paper we present the workflow we have developed for the semi-automatic segmentation of liver tumors in the datasets provided for the MICCAI Liver Tumor Segmentation contest. Since we wanted to develop a system that could be as automatic as possible and to follow the segmentation process in every single step starting from the image loading to the lesion extraction, we decided to subdivide the workflow in two main steps: first we focus on the segmentation of the liver and once we have extracted the organ structure we segment the lesions applying an adaptive multi-thresholding system.

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LIVER TUMOR SEGMENTATION CT DATA BASED ON ALEXNET-LIKE CONVOLUTION NEURAL NETS

10.18287/1613-0073-2016-1638-348-356 ◽

2016 ◽

Author(s):

Alexandr N. Korabelnikov ◽

◽

Alexandr V. Kolsanov ◽

Sergey S. Chaplygin ◽

Pavel M. Zelter ◽

...

Keyword(s):

Liver Tumors ◽

Automatic Segmentation ◽

A Priori ◽

Ground Truth ◽

Neural Nets ◽

Average Error ◽

Tumor Segmentation ◽

Ct Data ◽

And Performance ◽

Anatomical Information

Anatomical structure segmentation on computed tomography (CT) is the key stage in medical visualization and computer diagnosis. Tumors are one of types of internal structures, for which the problem of automatic segmentation today has no solution fully satisfying by quality. The reason is high variance of tumor’s density and inability of using a priori anatomical information about shape. In this paper we propose automatic method of liver tumors segmentation based on convolution neural nets (CNN). Studying and validation have been performed on set of CT with liver and tumors segmentation ground truth. Average error (VOE) by cross-validation is 17.3%. Also there were considered algorithms of pre- and post-processing which increase accuracy and performance of segmentation procedure. Particularly the acceleration of the segmentation procedure with negligible decrease of quality has been reached 6 times.

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A Deep-Learning Model with Learnable Group Convolution and Deep Supervision for Brain Tumor Segmentation

Mathematical Problems in Engineering ◽

10.1155/2021/6661083 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Hengxin Liu ◽

Qiang Li ◽

I-Chi Wang

Keyword(s):

Automatic Segmentation ◽

Complex Model ◽

Treatment Manual ◽

Tumor Segmentation ◽

Feature Maps ◽

Output Stage ◽

Convolutional Network ◽

Brain Tumor Segmentation ◽

Proposed Model ◽

Network Output

The segmentation of brain tumors in medical images is a crucial step of clinical treatment. Manual segmentation is time consuming and labor intensive, and existing automatic segmentation methods suffer from issues such as numerous parameters and low precision. To resolve these issues, this study proposes a learnable group convolution-based segmentation method that replaces convolution in the feature extraction stage with learnable group convolution, thereby reducing the number of convolutional network parameters and enhancing communication between convolution groups. To improve utilization of the feature maps, we added a skip connection structure between learnable group convolution modules, which increased segmentation precision. We used deep supervision to combine output images in the network output stage to reduce overfitting and enhance the recognition capabilities of the network. We tested the proposed algorithm model using the open BraTS 2018 dataset. The experiment results revealed that the proposed model is superior to 3D U-Net and DMFNet and has better segmentation results for tumor cores than No New-Net and NVDLMED, the winning methods in the BraTS 2018 challenge. The segmentation precision of the proposed method with regard to whole tumors, enhancing tumors, and tumor cores was 90.25%, 80.36%, and 86.20%. Furthermore, the proposed method uses fewer parameters and a less complex model.

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MFAN: Multi-Level Features Attention Network for Fake Certificate Image Detection

Entropy ◽

10.3390/e24010118 ◽

2022 ◽

Vol 24 (1) ◽

pp. 118

Author(s):

Yu Sun ◽

Rongrong Ni ◽

Yao Zhao

Keyword(s):

Contextual Information ◽

Feature Maps ◽

Attention Network ◽

Final Layer ◽

Spatial Pyramid Pooling ◽

Novel Method ◽

Feature Attention ◽

Multi Level ◽

The One ◽

Small Targets

Up to now, most of the forensics methods have attached more attention to natural content images. To expand the application of image forensics technology, forgery detection for certificate images that can directly represent people’s rights and interests is investigated in this paper. Variable tampered region scales and diverse manipulation types are two typical characteristics in fake certificate images. To tackle this task, a novel method called Multi-level Feature Attention Network (MFAN) is proposed. MFAN is built following the encoder–decoder network structure. In order to extract features with rich scale information in the encoder, on the one hand, we employ Atrous Spatial Pyramid Pooling (ASPP) on the final layer of a pre-trained residual network to capture the contextual information at different scales; on the other hand, low-level features are concatenated to ensure the sensibility to small targets. Furthermore, the resulting multi-level features are recalibrated on channels for irrelevant information suppression and enhancing the tampered regions, guiding the MFAN to adapt to diverse manipulation traces. In the decoder module, the attentive feature maps are convoluted and unsampled to effectively generate the prediction mask. Experimental results indicate that the proposed method outperforms some state-of-the-art forensics methods.

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