Deep learning-based histopathological analysis and its potential application in marine monitoring: A review and case study

doi:10.3969/j.issn.1001-909X.2024.03.005

Abstract

Abstract:

Histopathological indexes can be used to assess health of organism, but their application faces challenges such as low efficiency, high cost and strong subjectivity. Introducing artificial intelligence (AI) technology into histopathological analysis of biological tissues can leverage its high-throughput image analysis capabilities, overcoming the limitations in assessing and monitoring marine organism health. Based on our review on health assessment indicators of marine organisms, the application of AI in image analysis, and the use of AI for histopathological image processing, a deep learning-based histopathological image analysis approach was proposed using gill tissues of marine mussels as representative. Through a series of processes such as training, validation, and prediction of histopathology images, it was determined that the Res-UNet deep learning model can efficiently and accurately quantify histopathological damage in mussels’ gills. An automated, high-throughput, and less subjective workflow based on deep learning was finally established, offering new ideas and techniques for marine organism health assessment and marine monitoring.

Key words: artificial intelligence, neural networks, histopathological image processing, biological health assessment, marine model organisms, marine mussels, quantitative histopathology, gill filament area

CLC Number:

P714

DI Ya’nan, ZHAO Ruoxuan, XU Jianzhou. Deep learning-based histopathological analysis and its potential application in marine monitoring: A review and case study[J]. Journal of Marine Sciences, 2024, 42(3): 64-74.

Figures/Tables 6

Fig.1 Histopathological images of human and aquatic organisms

Fig.2 The quantitative indicators for digestive gland and gill tissues of mussels established by our team[67-68]

Fig.3 Schematic annotation of gill histopathologic images of marine mussels

Tab.1 Performance evaluation of image segmentation based on different models

评价指标	模型架构			骨架网络
评价指标	UNet	LinkNet	FPN	VGG16-UNet	ResNet34-UNet	EfficientNetB0-UNet
准确率	0.898 5±0.086 25	0.838 4±0.041 68	0.744 9±0.116 4	0.894 4±0.083 56	0.947 8±0.061 92	0.914 4±0.093 33
IoU	0.745 5±0.097 53	0.718 9±0.077 96	0.713 3±0.117 9	0.787 5±0.099 64	0.852 6±0.113 3	0.793 3±0.120 2
Dice系数	0.850 7±0.065 17	0.808 8±0.090 58	0.827 4±0.079 94	0.877 7±0.064 56	0.915 9±0.078 76	0.885 8±0.081 20
HD95	23.24±24.68	25.74±23.54	49.78±35.62	18.66±27.22	10.92±27.84	21.00±34.67

Fig.4 The histopathological alterations in gills of marine mussels based on different analyzing methods

Fig.5 Deep learning-based workflow for histopathological image analysis of marine organisms

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