Liver Semantic Segmentation Algorithm Based on Improved Deep Adversarial Networks in Combination of Weighted Loss Function on Abdominal CT Images

Xia, Kaijian; Yin, H.; Qian, Pengjiang; Jiang, Yizhang; Wang, Shuihua

doi:10.1109/access.2019.2929270

Cited by 96 publications

(45 citation statements)

References 23 publications

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“…For the task of semantic segmentation, Deeplab v3+ (Chen et al, 2018) is one of the most popular architectures, and the top performer on several different datasets; including generic consumer pictures, e.g., PASCAL VOC (Everingham et al, 2015), but also more specific ones like the recent ModaNet (Zheng et al, 2018), a large collection of street fashion images. Also in medical image analysis, Deeplab v3+ has been used to segment clinical image data, e.g., lesions of the liver in abdominal CT images (Xia et al, 2019). Remote sensing examples include detection of oil spills in satellite images (Krestenitis et al, 2019) to combat illegal discharges and tank cleaning that pollute the oceans.…”

Section: Related Workmentioning

confidence: 99%

Lake Ice Monitoring With Webcams and Crowd-Sourced Images

Prabha¹,

Tom

Rothermel³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatio-temporal information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model’s ability to generalise with data from multiple camera views and two different winters. On average, it achieves Intersection-over-Union (IoU) values of ≈71% across different cameras and ≈69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing websites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations.

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Section: Related Workmentioning

confidence: 99%

Lake Ice Monitoring With Webcams and Crowd-Sourced Images

Prabha¹,

Tom

Rothermel³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Deeplab ability to reach outstanding results makes it rapidly spread in many applications like with Kaijian Xia et al [22] who proposed an improved model based on the basic framework of DeepLab-V3, and Pix2pix network is introduced as the generation adversarial model for Liver Semantic Segmentation. Manu Goyal et al [23] introduced a method for ROI detection for a skin lesion using FRCNN-Inception-V2 and SSD-Inception-V2 and they compared the performance of skin localization methods with the state-of-the-art Deeplab segmentation method.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans

2020

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This research is concerned with malignant pulmonary nodule detection (PND) in low-dose CT scans. Due to its crucial role in the early diagnosis of lung cancer, PND has considerable potential in improving the survival rate of patients. We propose a two-stage framework that exploits the ever-growing advances in deep neural network models, and that is comprised of a semantic segmentation stage followed by localization and classification. We employ the recently published DeepLab model for semantic segmentation, and we show that it significantly improves the accuracy of nodule detection compared to the classical U-Net model and its most recent variants. Using the widely adopted Lung Nodule Analysis dataset (LUNA16), we evaluate the performance of the semantic segmentation stage by adopting two network backbones, namely, MobileNet-V2 and Xception. We present the impact of various model training parameters and the computational time on the detection accuracy, featuring a 79.1% mean intersection-over-union (mIoU) and an 88.34% dice coefficient. This represents a mIoU increase of 60% and a dice coefficient increase of 30% compared to U-Net. The second stage involves feeding the output of the DeepLab-based semantic segmentation to a localization-then-classification stage. The second stage is realized using Faster RCNN and SSD, with an Inception-V2 as a backbone. On LUNA16, the two-stage framework attained a sensitivity of 96.4%, outperforming other recent models in the literature, including deep models. Finally, we show that adopting a transfer learning approach, particularly, the DeepLab model weights of the first stage of the framework, to infer binary (malignant-benign) labels on the Kaggle dataset for pulmonary nodules achieves a classification accuracy of 95.66%, which represents approximately 4% improvement over the recent literature.

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“…It often occurs once a semester and is not rigorous. In summary, this research proposes an automatic anxiety recognition method based on deep features and machine learning [19][20][21][22][23][24][25][26][27]. The main work of this paper is summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Anxiety Recognition of College Students Using a Takagi-Sugeno-Kang Fuzzy System Modeling Method and Deep Features

Meng

Zhang

2020

IEEE Access

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College students are the most active, most sensitive, and most prone group with respect to various psychological problems in contemporary society. In recent years, with the intensification of social competition, including various pressures such as studies, examinations, economic loss, emotional loss, and employment, the incidence of anxiety, depression and suicide rates has increased. To effectively pay attention to the psychological development of college students and to strengthen mental health education, this research proposes a method to automatically identify the anxiety of college students using a Takagi-Sugeno-Kang (TSK) fuzzy system and deep features. First, preprocess the collected EEG of college students. Secondly, use convolutional neural network (CNN) to extract deep features from the input data. Finally, TSK fuzzy system is used to classify features to obtain the final recognition result. Through experiments on standard data sets and self-made data sets, the experimental results verify the superiority of the anxiety identification method used in this study. The experimental results further demonstrate that the depth features have richer information than traditional features. The noise immunity of TSK fuzzy system makes it show good classification performance and generalization. The recognition results can quickly locate students with anxiety disorders and narrow the scope of investigation for students with psychological problems. The automatic recognition of college students' anxiety can improve the efficiency of schools and teachers in investigating students' psychological problems. This research has very good practical application value.

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Liver Semantic Segmentation Algorithm Based on Improved Deep Adversarial Networks in Combination of Weighted Loss Function on Abdominal CT Images

Cited by 96 publications

References 23 publications

Lake Ice Monitoring With Webcams and Crowd-Sourced Images

Lake Ice Monitoring With Webcams and Crowd-Sourced Images

A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans

Anxiety Recognition of College Students Using a Takagi-Sugeno-Kang Fuzzy System Modeling Method and Deep Features

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