Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis

Cancian, Pierandrea; Cortese, Nina; Donadon, Matteo; Maio, Marco Di; Soldani, Cristiana; Marchesi, Federica; Savevski, Victor; Santambrogio, Marco D.; Cerina, Luca; Laino, Maria Elena; Torzilli, Guido; Mantovani, Alberto; Terracciano, Luigi; Roncalli, Massimo; Tommaso, Luca Di

doi:10.3390/cancers13133313

Cited by 9 publications

(9 citation statements)

References 33 publications

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“…Deep learning techniques demonstrate considerable bene ts in terms of precision and e ciency, due to the signi cant variations in the morphological characteristics of macrophages. The deep-learning pipeline, which is based on the innovative DeepLab-v3 architecture and semantic segmentation technique proposed by Cancian a et al [24], effectively enables the isolation of tumor-associated macrophages (TAMs) from the background and facilitates the identi cation of individual TAMs. Ong et al [11] compared the accuracy and speed of the model, which was developed using YOLOv5, to the current standards such as the Watershed/Gaussian mixture model (GMM) method and Cellpose technique in the context of macrophage cell detection during drug activity investigation.…”

Section: Macrophage Segmentationmentioning

confidence: 99%

Multi-task Deep Learning Pipeline for Irregular Macrophage Segmentation fusing Enhanced Microscopy Images and Auxiliary Boundary Maps

Zheng,

Lei,

Zhao

et al. 2024

Preprint

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Background:Accurate segmentation of macrophages from microscope images can be beneficial for understanding viral infection and immune development stages. There are two particularly challenging aspects in this task: (1) over-segmentation of macrophage with pseudopodia; (2)under-segmentation for clustering, overlapping macrophages and unclear boundary macrophages. Method: This paper proposes a microscope image enhancement multi-task deep learning framework to achieve segmentation of macrophages with complex boundary conditions. The network initially utilizes PENet to enhance the quality of microscope image data, improving the overall image quality. Then, a multi-task U-Net architecture is employed to extract crucial feature information from masks, distance transforms, and heatmaps. By utilizing the cell segmentation achieved through masks, the distance transforms and heatmaps are used to further refine and capture the intricate boundary details of macrophages, including pseudopodia and other irregularities. Results: Despite the challenges posed by partially or entirely obscured cells, the network demonstrates robust segmentation capabilities for surface-visible cells, achieving an accuracy of 61.24%, a precision of 78.79%, and a recall of 87.93%, outperforming some other segmentation networks, including SOTA Cellpose. Through experiments, it is possible to achieve precise segmentation of irregular boundaries and narrow pseudopodia of macrophages in low-quality microscope images. Conclusions: Compared to current macrophage segmentation techniques, this network has two significant advantages: (1) supplementing rich boundary detail information to capture the microscopic features of macrophageswith elongated pseudopods; (2) enhancing underexposed cells due to limitations of microscopic imaging techniques and capturing their potential information.

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Section: Macrophage Segmentationmentioning

confidence: 99%

Multi-task Deep Learning Pipeline for Irregular Macrophage Segmentation fusing Enhanced Microscopy Images and Auxiliary Boundary Maps

Zheng,

Lei,

Zhao

et al. 2024

Preprint

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“…No studies used prospectively collected data. The algorithms primary goal was either segmentation (6/22) [11][12][13][14][15][16], classification (6/22) [17][18][19][20][21][22] or prediction (10/22) [23][24][25][26][27][28][29][30][31][32]. The majority of studies (18/22) were focused on hepatocellular carcinoma.…”

Section: Tumoralmentioning

confidence: 99%

Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review

et al. 2023

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Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. Objective: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. Results: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. Conclusions: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

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“…By examining the available related literature, only two studies were found. In one study, Cancian P et al [ 101 ] attempted to employ a deep-learning pipeline to characterize TAMs in colorectal cancer liver metastasis. Through their comparisons, the authors found that AI can totally and successfully recognize TAMs embedded in the tumor microenvironment.…”

Section: Artificial Intelligence (Ai) and Tamsmentioning

confidence: 99%

Small Tweaks, Major Changes: Post-Translational Modifications That Occur within M2 Macrophages in the Tumor Microenvironment

Liang

Tan

et al. 2022

Cancers

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The majority of proteins are subjected to post-translational modifications (PTMs), regardless of whether they occur in or after biosynthesis of the protein. Capable of altering the physical and chemical properties and functions of proteins, PTMs are thus crucial. By fostering the proliferation, migration, and invasion of cancer cells with which they communicate in the tumor microenvironment (TME), M2 macrophages have emerged as key cellular players in the TME. Furthermore, growing evidence illustrates that PTMs can occur in M2 macrophages as well, possibly participating in molding the multifaceted characteristics and physiological behaviors in the TME. Hence, there is a need to review the PTMs that have been reported to occur within M2 macrophages. Although there are several reviews available regarding the roles of M2 macrophages, the majority of these reviews overlooked PTMs occurring within M2 macrophages. Considering this, in this review, we provide a review focusing on the advancement of PTMs that have been reported to take place within M2 macrophages, mainly in the TME, to better understand the performance of M2 macrophages in the tumor microenvironment. Incidentally, we also briefly cover the advances in developing inhibitors that target PTMs and the application of artificial intelligence (AI) in the prediction and analysis of PTMs at the end of the review.

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Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis

Cited by 9 publications

References 33 publications

Multi-task Deep Learning Pipeline for Irregular Macrophage Segmentation fusing Enhanced Microscopy Images and Auxiliary Boundary Maps

Multi-task Deep Learning Pipeline for Irregular Macrophage Segmentation fusing Enhanced Microscopy Images and Auxiliary Boundary Maps

Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review

Small Tweaks, Major Changes: Post-Translational Modifications That Occur within M2 Macrophages in the Tumor Microenvironment

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