Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning

Ahmad, Mubashir; Qadri, Syed Furqan; Ashraf, Muhammad Usman; Subhi, Khalid; Khan, Salabat; Zareen, Syeda Shamaila; Qadri, Salman

doi:10.1155/2022/2665283

Cited by 28 publications

(19 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting that Qadri et al [ 49 , 50 ] advocated the clinical decision support system using a learning algorithm to further improve or reflect the application value of the proposed method. Therefore, shortly after the completion of this study, the author began to design the audio collection terminal placed in the broiler captivity area and visualization software.…”

Section: Discussionmentioning

confidence: 99%

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Tao

Wang

Sun

et al. 2022

Sensors

View full text Add to dashboard Cite

In view of the limited number of extracted sound features, the lack of in-depth analysis of applicable sound features, and the lack of in-depth study of the selection basis and optimization process of classification models in the existing broiler sound classification or recognition research, the author proposes a recognition method for broiler sound signals based on multi-domain sound features and classification models. The implementation process is divided into the training stage and the testing stage. In the training stage, the experimental area is built, and multiple segments of broiler sound signals are collected and filtered. Through sub-frame processing and endpoint detection, the combinations of start frames and end frames of multiple sound types in broiler sound signals are obtained. A total of sixty sound features from four aspects of time domain, frequency domain, Mel-Frequency Cepstral Coefficients (MFCC), and sparse representation are extracted from each frame signal to form multiple feature vectors. These feature vectors are labeled manually to build the data set. The min-max standardization method is used to process the data set, and the random forest is used to calculate the importance of sound features. Then, thirty sound features that contribute more to the classification effect of the classification model are retained. On this basis, the classification models based on seven classification algorithms are trained, the best-performing classification model based on k-Nearest Neighbor (kNN) is obtained, and its inherent parameters are optimized. Then, the optimal classification model is obtained. The test results show that the average classification accuracy achieved by the decision-tree-based classifier (abbreviated as DT classifier) on the data set before and after min–max standardization processing is improved by 0.6%, the average classification accuracy achieved by the DT classifier on the data set before and after feature selection is improved by 3.1%, the average classification accuracy achieved by the kNN-based classification model before and after parameter optimization is improved by 1.2%, and the highest classification accuracy is 94.16%. In the testing stage, for a segment of the broiler sound signal collected in the broiler captivity area, the combinations of the start frames and end frames of multiple sound types in the broiler sound signal are obtained through signal filtering, sub-frame processing, endpoint detection, and other steps. Thirty sound features are extracted from each frame signal to form the data set to be predicted. The optimal classification model is used to predict the labels of each piece of data in the data set to be predicted. By performing majority voting processing on the predicted labels of the data combination corresponding to each sound type, the common labels are obtained; that is, the predicted types are obtained. On this basis, the definition of recognition accuracy for broiler sound signals is proposed. The test results show that the classification accuracy achieved by the optimal classification model on the data set to be predicted is 93.57%, and the recognition accuracy achieved on the multiple segments of the broiler sound signals is 99.12%.

show abstract

Section: Discussionmentioning

confidence: 99%

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Tao

Wang

Sun

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…It has shown a DICE score of 95,64% on the LiTS dataset. Ahmad et al [30] explored patch-based stacked autoencoder (SAE) for liver segmentation to overcome the problem of fuzzy boundaries that leads to poor segmentation. The method achieved a Dice score of 96.47 % on the MICCAI-Sliver'07.…”

Section: Literuture Reviewmentioning

confidence: 99%

Deep Learning based Liver Segmentation: A Review

Napte¹,

Mahajan²

2022

RIA

View full text Add to dashboard Cite

Tremendous advancement takes place in the field of medical science. With this advancement, it is possible to support diagnosis and treatment planning for various diseases related to the abdominal organ. The liver is one of the adnominal organs, a common site for developing tumors. Liver disease is one of the main causes of death. Due to its complex and heterogeneous nature and shape, it is challenging to segment the liver and its tumor. There are numerous methods available for liver segmentation. Some are handcrafted, semi-automatic, and fully automatic. Image segmentation using deep learning techniques is becoming a very robust tool nowadays. There are many methods of liver segmentation which uses Deep Learning. This article provides the survey of the various liver segmentation schemes based on Artificial Neural Network (ANN), Convolution Neural network (CNN), Deep Belief network (DBN), Auto Encoder, Deep Feed-forward neural Network (DFNN), etc based on the architecture details, methodology, performance metrics and dataset details. Researchers are continuously putting efforts into improving these segmentation techniques. So this article give out a comprehensive review of deep learning-based liver segmentation techniques and highlights the advantages of the deep learning segmentation schemes over the traditional segmentation techniques.

show abstract

“…With the advancement of deep learning technology in recent years, integrating medical images and artificial intelligence has emerged as a popular study area in medicine [ 19 ]. Convolutional neural networks (CNNs) have rapidly gained popularity as a powerful tool for many image processing applications, including classification, object identification, segmentation, and registration, among others [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction

et al. 2022

View full text Add to dashboard Cite

Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measurements, anatomy structure, fetal movements, and pregnancy complications. Recent developments in artificial intelligence and computer vision provide new methods for the automated analysis of medical images in many domains, including ultrasound images. We present a full end-to-end framework for segmenting, measuring, and estimating fetal gestational age and weight based on two-dimensional ultrasound images of the fetal head. Our segmentation framework is based on the following components: (i) eight segmentation architectures (UNet, UNet Plus, Attention UNet, UNet 3+, TransUNet, FPN, LinkNet, and Deeplabv3) were fine-tuned using lightweight network EffientNetB0, and (ii) a weighted voting method for building an optimized ensemble transfer learning model (ETLM). On top of that, ETLM was used to segment the fetal head and to perform analytic and accurate measurements of circumference and seven other values of the fetal head, which we incorporated into a multiple regression model for predicting the week of gestational age and the estimated fetal weight (EFW). We finally validated the regression model by comparing our result with expert physician and longitudinal references. We evaluated the performance of our framework on the public domain dataset HC18: we obtained 98.53% mean intersection over union (mIoU) as the segmentation accuracy, overcoming the state-of-the-art methods; as measurement accuracy, we obtained a 1.87 mm mean absolute difference (MAD). Finally we obtained a 0.03% mean square error (MSE) in predicting the week of gestational age and 0.05% MSE in predicting EFW.

show abstract

Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning

Cited by 28 publications

References 66 publications

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Deep Learning based Liver Segmentation: A Review

Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction

Contact Info

Product

Resources

About