Practical utility of liver segmentation methods in clinical surgeries and interventions

Ansari, Mohammed Yusuf; Abdalla, Alhusain; Ansari, Mohammed Yaqoob; Malluhi, Byanne; Mohanty, Sasmita; Mishra, Subhashree; Singh, Sudhansu Sekhar; Abinahed, Julien; Al‐Ansari, Abdulla; Balakrishnan, Shidin; Dakua, Sarada Prasad

doi:10.1186/s12880-022-00825-2

Cited by 37 publications

(25 citation statements)

References 119 publications

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“…Over the years, many conventional 3 – 5 and deep learning-based 6 – 8 segmentation algorithms have been proposed to overcome the challenges in CT scans and maximize segmentation accuracy. However, the methods have not emphasized maximizing performance in disk and memory-constrained environments.…”

Section: Introductionmentioning

confidence: 99%

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

Ansari

Yang

Balakrishnan

et al. 2022

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

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

Ansari

Yang

Balakrishnan

et al. 2022

Sci Rep

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“…Following the publication of the original article [ 1 ] the authors requested to remove the statement "The Open Access funding was provided by Qatar National Library" from the "Funding" section of their article, as this is no longer applicable.…”

Section: Correction To: Bmc Medical Imaging (2022) 22:97 101186/s1288...mentioning

confidence: 99%

Correction: Practical utility of liver segmentation methods in clinical surgeries and interventions

et al. 2022

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“…There is also a noticeable lack of model benchmarking within the related literature, with supervised ML models being arbitrarily chosen to perform a given classification task (Table S1 in Supporting Information ). In addition, several studies embrace DL models, despite the “excellent” performance of ML models (Abedini et al., 2018; Ansari, Abdalla, et al., 2022; Borazjani et al., 2016; Mollajan et al., 2016; Sharifi, 2022). The associated data sets do not meet the typical class balance and quantity requirements to ensure DL model generalizability.…”

Section: Introductionmentioning

confidence: 99%

An Object‐Based Approach to Differentiate Pores and Microfractures in Petrographic Analysis Using Explainable, Supervised Machine Learning

Jayachandran,

Gibbs,

Laya

et al. 2024

Earth and Space Science

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Petrographic observations are vital for carbonate pore‐typing, linking geological frameworks to petrophysical behavior. However, current petrographic pore typing is manual, with the qualitative to semi‐quantitative results not easily fitted into quantitative subsurface characterization. Some recent studies have automated this process using supervised machine learning (ML) and deep learning (DL), focusing on simple pore morphological features, and have reported high classification accuracies for several complex pore types. However, there are concerns about the validity of these studies due to conceptual and technical flaws in their collective approach. This study was aimed at a more fundamental problem, classifying between open microfractures and open pores in petrographic thin sections using an object‐based approach and explainable supervised ML. We analyzed 18 carbonate thin sections from the USA, numerically representing them using five shape features: compactness, aspect ratio, extent, solidity, and formfactor. Using a labeled data set of 400 microfractures and 400 pores, we evaluated nine of the most widely used supervised models. All models showed high testing accuracies (89.58%–90.42%). Interestingly, complex non‐linear models did not significantly outperform simpler linear ones. Compactness and aspect ratio were the most informative features. However, the labeled data sets did not reflect the overall data set's complexity, which suggested that high accuracies in similar studies might be due to curated data sets rather than accounting for the true complexity of carbonate pore systems. The study concludes that simple shape features are ineffective for classifying carbonate pore types. It is hoped that this study will provide a foundation for more robust artificial intelligence‐assisted pore typing.

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Practical utility of liver segmentation methods in clinical surgeries and interventions

Cited by 37 publications

References 119 publications

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

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

Correction: Practical utility of liver segmentation methods in clinical surgeries and interventions

An Object‐Based Approach to Differentiate Pores and Microfractures in Petrographic Analysis Using Explainable, Supervised Machine Learning

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