Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation

Rettenberger, Luca; Schilling, Marcel P.; Elser, Stefan; Böhland, Moritz; Reischl, Markus

doi:10.1109/tbme.2023.3252889

Cited by 5 publications

(1 citation statement)

References 57 publications

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“…Deep learning is well-known and has been used in previous biomedical studies (Kha et al, 2022 ; Yuan et al, 2023 ). For example, SSL-based segmentation applications on biomedical microscopic images were highly adopted (Shurrab and Duwairi, 2022 ; Rettenberger et al, 2023 ; Sánchez et al, 2023 ). However, gathering sufficient volumes of annotated microscopy images is a tedious and time-consuming task that requires considerable domain expertise.…”

Section: Related Workmentioning

confidence: 99%

Super resolution-based methodology for self-supervised segmentation of microscopy images

Bommanapally,

Abeyrathna,

Chundi

et al. 2024

Front. Microbiol.

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Data-driven Artificial Intelligence (AI)/Machine learning (ML) image analysis approaches have gained a lot of momentum in analyzing microscopy images in bioengineering, biotechnology, and medicine. The success of these approaches crucially relies on the availability of high-quality microscopy images, which is often a challenge due to the diverse experimental conditions and modes under which these images are obtained. In this study, we propose the use of recent ML-based image super-resolution (SR) techniques for improving the image quality of microscopy images, incorporating them into multiple ML-based image analysis tasks, and describing a comprehensive study, investigating the impact of SR techniques on the segmentation of microscopy images. The impacts of four Generative Adversarial Network (GAN)- and transformer-based SR techniques on microscopy image quality are measured using three well-established quality metrics. These SR techniques are incorporated into multiple deep network pipelines using supervised, contrastive, and non-contrastive self-supervised methods to semantically segment microscopy images from multiple datasets. Our results show that the image quality of microscopy images has a direct influence on the ML model performance and that both supervised and self-supervised network pipelines using SR images perform better by 2%–6% in comparison to baselines, not using SR. Based on our experiments, we also establish that the image quality improvement threshold range [20–64] for the complemented Perception-based Image Quality Evaluator(PIQE) metric can be used as a pre-condition by domain experts to incorporate SR techniques to significantly improve segmentation performance. A plug-and-play software platform developed to integrate SR techniques with various deep networks using supervised and self-supervised learning methods is also presented.

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Section: Related Workmentioning

confidence: 99%

Super resolution-based methodology for self-supervised segmentation of microscopy images

Bommanapally,

Abeyrathna,

Chundi

et al. 2024

Front. Microbiol.

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Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images

Arabi,

Zaidi

2024

J Digit Imaging. Inform. med.

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To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning techniques are employed, particularly when the size of the training dataset is limited. In this work, we set out to investigate the impact of contrastive learning and self-learning on the performance of the deep learning-based semantic segmentation. To this end, three different datasets were employed used for brain tumor and hippocampus delineation from MR images (BraTS and Decathlon datasets, respectively) and kidney segmentation from CT images (Decathlon dataset). Since data augmentation techniques are also aimed at enhancing the performance of deep learning methods, a deformable data augmentation technique was proposed and compared with contrastive learning and self-learning frameworks. The segmentation accuracy for the three datasets was assessed with and without applying data augmentation, contrastive learning, and self-learning to individually investigate the impact of these techniques. The self-learning and deformable data augmentation techniques exhibited comparable performance with Dice indices of 0.913 ± 0.030 and 0.920 ± 0.022 for kidney segmentation, 0.890 ± 0.035 and 0.898 ± 0.027 for hippocampus segmentation, and 0.891 ± 0.045 and 0.897 ± 0.040 for lesion segmentation, respectively. These two approaches significantly outperformed the contrastive learning and the original model with Dice indices of 0.871 ± 0.039 and 0.868 ± 0.042 for kidney segmentation, 0.872 ± 0.045 and 0.865 ± 0.048 for hippocampus segmentation, and 0.870 ± 0.049 and 0.860 ± 0.058 for lesion segmentation, respectively. The combination of self-learning with deformable data augmentation led to a robust segmentation model with no outliers in the outcomes. This work demonstrated the beneficial impact of self-learning and deformable data augmentation on organ and lesion segmentation, where no additional training datasets are needed.

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AI integration in construction safety: Current state, challenges, and future opportunities in text, vision, and audio based applications

Rabbi,

Jeelani

2024

Automation in Construction

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Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation

Cited by 5 publications

References 57 publications

Super resolution-based methodology for self-supervised segmentation of microscopy images

Super resolution-based methodology for self-supervised segmentation of microscopy images

Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images

AI integration in construction safety: Current state, challenges, and future opportunities in text, vision, and audio based applications

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