Dataset Information

Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.

ABSTRACT: Deep learning-based cell segmentation is increasingly utilized in cell biology due to the massive accumulation of large-scale datasets and excellent progress in model architecture and instance representation. However, the development of specialist algorithms has long been hampered by a paucity of annotated training data, whereas the performance of generalist algorithms is limited without experiment-specific calibration. Here, we present Scellseg, an adaptive pipeline that utilizes a style-aware pre-trained model coupled to a contrastive fine-tuning strategy that also learns from unlabeled data. Scellseg achieves state-of-the-art transferability in average precision and Aggregated Jaccard Index on disparate datasets containing microscopy images at three biological levels, from organelle, cell to organism. Interestingly, when fine-tuning Scellseg, we show that performance plateaued after approximately eight images, implying that a specialist model can be obtained with few manual efforts. For convenient dissemination, we develop a graphical user interface that allows biologists to easily specialize their self-adaptive segmentation model.

SUBMITTER: Xun D

PROVIDER: S-EPMC9678729 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.

Xun Dejin D Chen Deheng D Zhou Yitian Y Lauschke Volker M VM Wang Rui R Wang Yi Y

iScience 20221104 12

Deep learning-based cell segmentation is increasingly utilized in cell biology due to the massive accumulation of large-scale datasets and excellent progress in model architecture and instance representation. However, the development of specialist algorithms has long been hampered by a paucity of annotated training data, whereas the performance of generalist algorithms is limited without experiment-specific calibration. Here, we present Scellseg, an adaptive pipeline that utilizes a style-aware ...[more]

PMID: 36425762

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Project description:Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention than other tasks within the context of deep spectral methods. This paper addresses that not all channels of the feature map extracted from a self-supervised backbone contain sufficient information for instance segmentation purposes. Some channels are noisy and hinder the accuracy of the task. To overcome this issue, this paper proposes two channel reduction modules, Noise Channel Reduction (NCR) and Deviation-based Channel Reduction (DCR). The NCR retains channels with lower entropy, as they are less likely to be noisy, while DCR prunes channels with low standard deviation, as they lack sufficient information for effective instance segmentation. Furthermore, the paper demonstrates that the dot product, commonly used in deep spectral methods, is not suitable for instance segmentation due to its sensitivity to feature map values, potentially leading to incorrect instance segments. A novel similarity metric called Bray-curtis over Chebyshev (BoC) is proposed to address this issue. This metric considers the distribution of features in addition to their values, providing a more robust similarity measure for instance segmentation. Quantitative and qualitative results on the Youtube-VIS 2019 and OVIS datasets highlight the improvements achieved by the proposed channel reduction methods and using BoC instead of the conventional dot product for creating the affinity matrix. These improvements regarding mean Intersection over Union (mIoU) and extracted instance segments are observed, demonstrating enhanced instance segmentation performance. The code is available on: https://github.com/farnooshar/SpecUnIIS.

Dataset Information

Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.

Publications

Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.

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