Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures

Wei, Xiaoqin; Chen, Xiaowen; Lai, Ce; Yuan-zhong, Zhu; Yang, Han Feng; Du, Yong

doi:10.1155/2021/9956983

Cited by 19 publications

(9 citation statements)

References 54 publications

(67 reference statements)

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“…The authors [33] proposed a liver image segmentation approach that combines GANs with Mask R-CNN. Initially, due to the presence of noisy elements in the majority of images, the researchers explored the integration of Mask R-CNN and GANs to enhance the precision of pixel-wise classification.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Hendi,

Hossain,

Majrashi

et al. 2024

Applied Sciences

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The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.

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Section: Related Workmentioning

confidence: 99%

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Hendi,

Hossain,

Majrashi

et al. 2024

Applied Sciences

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“…Wei et al used a combination of GAN and Masks R-CNN to segment the liver from CT images. In the improved mask R-CNN, the k-means algorithm was utilized to adjust the bounding box parameters using a Euclidean distance [ 46 ]. The GAN-based approach yielded an average DSC of 95.3% while evaluating 378 CT images.…”

Section: Medical Image Analysis Using Deep Learningmentioning

confidence: 99%

A Review Paper about Deep Learning for Medical Image Analysis

Sistaninejhad,

Rasi,

Nayeri

2023

Computational and Mathematical Methods in Medicine

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Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks’ performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction.

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“…However, these techniques rely on handcrafted characteristics and can only partially convey the features. Fully convolutional neural networks (FCNs) have recently achieved excellent results in a variety of cognitive difficulties [5], [6]. The literature may be generally separated into two groups since many researchers are advocating this collection of deep learning algorithms for issues with liver and tumour segmentation i) 2DFCN, including the UNet architecture [7], multi-channel FCN [8], and FCN built on VGG16 [9] and ii) 3D FCN [10], where 3D convolution with volume data input has been used in place of 2D convolution.…”

Section: Introductionmentioning

confidence: 99%

Automatic liver segmentation in computed tomography scans using deep semantic segmentation

Ezzat

Mahdy²,

Seddawy³

2023

Bulletin EEI

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Division of the liver from figured computed tomography (CT) images is fundamental for the greater part of the PC supported clinical applications, for instance, the arranging period of a liver transfer, liver volume assessment, and radiotherapy. In this paper, a programmed liver location model from clinical CT filters utilizing profound semantic division convolutional neural organization will be introduced, this model will actually want to subsequently isolate the liver utilizing CT images. The proposed model presents simultaneously the liver ID and the probabilistic division utilizing a profound convolutional neural organization. The proposed approach was endorsed on 10 CT volumes taken from open data sets 3Dircadb1. The proposed model is totally programmed with no requirement for client mediation. Quantitative results show that proposed model is reliable and exact for hepatic volume assessment in a clinical course of action with testing exactness 98.8%.

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Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures

Cited by 19 publications

References 54 publications

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

A Review Paper about Deep Learning for Medical Image Analysis

Automatic liver segmentation in computed tomography scans using deep semantic segmentation

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