Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations

There is no denying how machine learning and computer vision have grown in the recent years. Their highest advantages lie within their automation, suitability, and ability to generate astounding results in a matter of seconds in a reproducible manner. This is aided by the ubiquitous advancements reached in the computing capabilities of current graphical processing units and the highly efficient implementation of such techniques. Hence, in this paper, we survey the key studies that are published between 2014 and 2020, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic-tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and further partitioned if the amount of works that fall under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites, containing masks of the aforementioned tissues, are thoroughly discussed, highlighting the organizers original contributions, and those of other researchers. Also, the metrics that are used excessively in literature are mentioned in our review stressing their relevancy to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing such as the scarcity of many studies on the vessels segmentation challenge, and why their absence needs to be dealt with in an accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver tissues segmentation based on automated ML-based technique

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites containing masks of the aforementioned tissues are thoroughly discussed, highlighting the organizers' original contributions and those of other researchers. Also, the metrics used excessively in the literature are mentioned in our review, stressing their relevance to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation challenge and why their absence needs to be dealt with sooner than later. 2022 The Author(s)This publication was made possible by an Award [GSRA6-2-0521-19034] from Qatar National Research Fund (a member of Qatar Foundation). The contents herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National LibraryScopu

Moving object tracking in clinical scenarios: application to cardiac surgery and cerebral aneurysm clipping.

BACKGROUND AND OBJECTIVES: Surgical procedures such as laparoscopic and robotic surgeries are popular since they are invasive in nature and use miniaturized surgical instruments for small incisions. Tracking of the instruments (graspers, needle drivers) and field of view from the stereoscopic camera during surgery could further help the surgeons to remain focussed and reduce the probability of committing any mistakes. Tracking is usually preferred in computerized video surveillance, traffic monitoring, military surveillance system, and vehicle navigation. Despite the numerous efforts over the last few years, object tracking still remains an open research problem, mainly due to motion blur, image noise, lack of image texture, and occlusion. Most of the existing object tracking methods are time-consuming and less accurate when the input video contains high volume of information and more number of instruments. METHODS:This paper presents a variational framework to track the motion of moving objects in surgery videos. The key contributions are as follows: (1) A denoising method using stochastic resonance in maximal overlap discrete wavelet transform is proposed and (2) a robust energy functional based on Bhattacharyya coefficient to match the target region in the first frame of the input sequence with the subsequent frames using a similarity metric is developed. A modified affine transformation-based registration is used to estimate the motion of the features following an active contour-based segmentation method to converge the contour resulted from the registration process. RESULTS AND CONCLUSION:The proposed method has been implemented on publicly available databases; the results are found satisfactory. Overlap index (OI) is used to evaluate the tracking performance, and the maximum OI is found to be 76% and 88% on private data and public data sequences

A lightweight neural network with multiscale feature enhancement for liver CT segmentation

Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.This publication was made possible by NPRP-11S-1219-170106 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work, and are solely the responsibility of the authors

Towards developing a segmentation method for cerebral aneurysm using a statistical multiresolution approach

Abstract The computer aided diagnosis (CAD) algorithms are considered crucial during the treatment planning of cerebral aneurysms (CA), where segmentation is the first and foremost step. This paper presents a segmentation algorithm in two-dimensional domain combining a multiresolution and a statistical approach. Precisely, Contourlet transform (CT) extracts the image features, while Hidden Markov Random Field with Expectation Maximization (HMRF-EM) segments the image, based on the spatial contextual constraints. The proposed algorithm is tested on Three-Dimensional Rotational Angiography (3DRA) datasets; the average values of accuracy, DSC, FPR, FNR, specificity, and sensitivity, are found to be 99.64%, 92.44%, 0.09%, 5.81%, 99.84%, and 93.22%, respectively. Both qualitative and quantitative results obtained show the potential of the proposed method

Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau vs OneCycleLR

Machine learning and computer vision techniques have influenced many fields including the biomedical one. The aim of this paper is to investigate the important concept of schedulers in manipulating the learning rate (LR), for the liver segmentation task, throughout the training process, focusing on the newly devised OneCycleLR against the ReduceLRonPlateau. A dataset, published in 2018 and produced by the Medical Segmentation Decathlon Challenge organizers, called Task 8 Hepatic Vessel (MSDC-T8) has been used for testing and validation. The reported results that have the same number of maximum epochs (75), and are the average of 5-fold cross-validation, indicate that ReduceLRonPlateau converges faster while maintaining a similar or even better loss score on the validation set when compared to OneCycleLR. The epoch at which the peak LR occurs perhaps should be made early for the OneCycleLR such that the super-convergence feature can be observed. Moreover, the overall results outperform the state-of-the-art results from the researchers who published the liver masks for this dataset. To conclude, both schedulers are suitable for medical segmentation challenges, especially the MSDC-T8 dataset, and can be used confidently in rapidly converging the validation loss with a minimal number of epochs. 2022, Springer Nature Switzerland AG.This publication was made possible by an Award [GSRA6-2-0521-19034] from Qatar National Research Fund (a member of Qatar Foundation). The contents herein are solely the responsibility of the authors. Moreover, the HPC resources and services used in this work were provided by the Research Computing group in Texas A&M University at Qatar. Research Computing is funded by the Qatar Foundation for Education, Science and Community Development (http://www.qf.org.qa).Scopu

Segmenting Liver Volume for Surgical Analysis

Introduction: Almost two million people worldwide die annually due to hepatic-related diseases. Half of these diseases are attributed to cirrhosis and the other half are related to hepatitis and hepatocellular carcinoma (HCC). The liver is also a metastasis hub from adjacent organs. This research aims to create an accurate high-quality delineation of the human liver and prepare them to be 3D printed for medical analysis to help aid medical practitioners in pre-procedural planning. Materials and Methods: Convolutional neural networks (ConvNets) are used to perform the liver tissues delineation. A famous ConvNet, named U-net, is used as the basis benchmark architecture that is also known for its great outcomes in the medical segmentation field. Contrast-enhanced computerized tomography (CT) scans are used from the famous Medical Segmentation Decathlon Challenge (Task 8: Hepatic Vessel), abbreviated as MSDC-T8. It contains 443 CT scans, which is considered the largest dataset that contains both the tumors and vessels ground-truth segmentation. Some researchers also generated the liver masks for this dataset, making it a complete dataset that contains all the relevant tissues' ground-truth masks. Results: Currently, the liver delineation has been successfully done with very high DICE = 98.12% (higher than the state-of-the-art results DICE = 97.61%), where a comparison between two famous schedulers namely, ReduceLRonPlateau and OneCycleLR has been conducted. Moreover, the 3D liver volume creation has also been completed and built via the marching cube algorithm. Conclusions/Future Directions: The developed ConvNet can segment livers with high confidence. The tumor(s) and vessels tissues segmentation are also under investigation now. Moreover, newly devised self-organized neural networks (Self-ONN) look promising and will be investigated soon. Lastly, a GUI will be built so that the medical practitioner can just insert the CT volume and get the 3D liver volume with all the segmented tissues