Evolution of Deep Learning Algorithms for MRI-Based Brain Tumor Image Segmentation

Shal, Khaled; Choudhry, Mahipal Singh

doi:10.1615/critrevbiomedeng.2021035557

Cited by 8 publications

(4 citation statements)

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“…Deep neural networks (DNNs) are now widely recognized as superior tools for cell segmentation 28 . Unlike traditional image processing, machine-learning approaches such as DNNs require training on a ground-truth dataset of cells and corresponding labels.…”

Section: Mainmentioning

confidence: 99%

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

et al. 2022

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Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.

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Section: Mainmentioning

confidence: 99%

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

et al. 2022

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“…Machine-learning based algorithms rely on training with labeled ground-true data and their performance is largely dependent on the quality and size of the training dataset. Among such algorithms, deep neural networks (DNNs) have emerged as superior tools for cell segmentation [ 55 , 56 ]. As several excellent studies have comprehensively introduced or compared these algorithms/software tools [ 31 , 32 , 56 , 57 , 58 , 59 ], we will not conduct a quantitative evaluation of their segmentation quality here.…”

Section: Quantification Methods Based On Microscopic Imagesmentioning

confidence: 99%

The Quantification of Bacterial Cell Size: Discrepancies Arise from Varied Quantification Methods

Cao

Huang

Zhang

et al. 2023

Life

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The robust regulation of the cell cycle is critical for the survival and proliferation of bacteria. To gain a comprehensive understanding of the mechanisms regulating the bacterial cell cycle, it is essential to accurately quantify cell-cycle-related parameters and to uncover quantitative relationships. In this paper, we demonstrate that the quantification of cell size parameters using microscopic images can be influenced by software and by the parameter settings used. Remarkably, even if the consistent use of a particular software and specific parameter settings is maintained throughout a study, the type of software and the parameter settings can significantly impact the validation of quantitative relationships, such as the constant-initiation-mass hypothesis. Given these inherent characteristics of microscopic image-based quantification methods, it is recommended that conclusions be cross-validated using independent methods, especially when the conclusions are associated with cell size parameters that were obtained under different conditions. To this end, we presented a flexible workflow for simultaneously quantifying multiple bacterial cell-cycle-related parameters using microscope-independent methods.

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“…Rathore et al employed high and low-grade tumors from The Cancer Imaging Archive (original images acquired 1983–2008) to extract an extensive set of engineered features (intensity, histogram, and texture) from delineated tumor regions on MRI and histopathologic images and used Cox proportional hazard regression and SVM models to MRI features only, histopathologic features only and combined MRI and histopathologic features and found that the combined model had higher accuracy in predicting OS as compared to either model in isolation (AUC 0.86) [ 91 ]. Ultimately, traditional ML-based methods do depend on several aspects including segmentation which does introduce both a component of workload as well as bias since the segmentation itself and the methods involved do dictate the signal that is eventually measured and interpreted [ 19 , 30 , 38 , 48 ]. It should also be noted that in the context of central nervous system tumors and other cancers treated with radiation therapy, the tumor volumes themselves are manually delineated to allow for targeting of the tumor with radiation therapy.…”

Section: Segmentationmentioning

confidence: 99%

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

AV¹,

Zhuge²,

Zhao³

et al. 2022

J Biotechnol Biomed

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The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, previous and ongoing treatment, and account for the dependency on reproducible human interpretation of multiple factors with incomplete data linkage. To standardize reporting, minimize bias, expedite management, and improve outcomes, the use of Artificial Intelligence (AI) has gained significant prominence in imaging analysis. In oncology, AI methods have as a result been explored in most cancer types with ongoing progress in employing AI towards imaging for oncology treatment, assessing treatment response, and understanding and communicating prognosis. Challenges remain with limited available data sets, variability in imaging changes over time augmented by a growing heterogeneity in analysis approaches. We review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid analyses, are being employed in AI-driven imaging analysis in central nervous system tumors. ML, DL, and hybrid approaches coexist, and their combination may produce superior results although data in this space is as yet novel, and conclusions and pitfalls have yet to be fully explored. We note the growing technical complexities that may become increasingly separated from the clinic and enforce the acute need for clinician engagement to guide progress and ensure that conclusions derived from AI-driven imaging analysis reflect that same level of scrutiny lent to other avenues of clinical research.

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Evolution of Deep Learning Algorithms for MRI-Based Brain Tumor Image Segmentation

Cited by 8 publications

References 0 publications

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

The Quantification of Bacterial Cell Size: Discrepancies Arise from Varied Quantification Methods

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

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