Nucleus segmentation: towards automated solutions

Hollandi, Réka; Moshkov, Nikita; Paavolainen, Lassi; Tasnádi, Ervin; Piccinini, Filippo; Horváth, Péter

doi:10.1016/j.tcb.2021.12.004

Cited by 43 publications

(41 citation statements)

References 86 publications

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“…Currently, segmentation tools used largely rely on nuclear segmentation, and those that incorporate membrane based segmentation are far from perfect [36, 37]. Once segmentation masks and measurements become more reliable in regards to shapes of cells, then morphological features of cells could be included in STELLAR as feature vectors in addition to the marker expression.…”

Section: Discussionmentioning

confidence: 99%

Annotation of Spatially Resolved Single-cell Data with STELLAR

Brbić

Cao

Hickey

et al. 2021

Preprint

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Spatial protein and RNA imaging technologies have been gaining rapid attention but current computational methods for annotating cells are based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method that utilizes spatial and molecular cell information to automatically assign cell types from an annotated reference set as well as discover new cell types and cell states. STELLAR transfers annotations across different dissection regions, tissues, and donors and detects higher-order tissue structures with dramatic time savings.

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Section: Discussionmentioning

confidence: 99%

Annotation of Spatially Resolved Single-cell Data with STELLAR

Brbić

Cao

Hickey

et al. 2021

Preprint

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“…Nucleus segmentation methods can be partitioned in two major groups: those that rely on classical image processing approaches and those that propose Deep Learning models. For a thorough review, we refer the reader to (Hollandi et al, 2022).…”

Section: Nuclei Segmentationmentioning

confidence: 99%

“…For example, in a recent publication (Haase et al, 2020) the authors propose first to denoise the images with Gaussian blur, second to separate regions using Voronoï tessellation, and to finally obtain a binary mask by applying an Otsu thresholding to obtain the segmentation. However, time-consuming parameter finetuning is required from the user at different steps of such classical image processing pipelines, making processing large amount of data impractical (Hollandi et al, 2022).…”

Section: Nuclei Segmentationmentioning

confidence: 99%

“…Recent deep-learning based tools such as Cellpose (Stringer et al, 2021) and StardDist (Schmidt et al, 2018) have greatly reduced the necessity to choose specific parameters. However, despite these important methodological advances, no single combination of methods and parameters can be adopted to automatically perform nuclei segmentation in all images, due to the aforementioned heterogeneity of biological samples and technical artifacts (Hollandi et al, 2022). In particular, live-cell imaging represents a stumbling block for these techniques, since these images are often acquired with low light levels and thus have very low SNR and artifacts.…”

Section: Introductionmentioning

confidence: 99%

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SalienceNet: an unsupervised Image-to-Image translation method for nuclei saliency enhancement in microscopy images

Bouilhol¹

2022

Preprint

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Automatic segmentation of nuclei in low-light microscopy images remains a difficult task, especially for high-throughput experiments where need for automation is strong. Low saliency of nuclei with respect to the background, variability of their intensity together with low signal-to-noise ratio in these images constitute a major challenge for mainstream algorithms of nuclei segmentation. In this work we introduce SalienceNet, an unsupervised deep learning-based method that uses the style transfer properties of cycleGAN to transform low saliency images into high saliency images, thus enabling accurate segmentation by downstream analysis methods, and that without need for any parameter tuning. We have acquired a novel dataset of organoid images with soSPIM, a microscopy technique that enables the acquisition of images in low-light conditions. Our experiments show that SalienceNet increased the saliency of these images up to the desired level. Moreover, we evaluated the impact of SalienceNet on segmentation for both Otsu thresholding and StarDist and have shown that enhancing nuclei with SalienceNet improved segmentation results using Otsu thresholding by 30% and using StarDist by 26% in terms of IOU when compared to segmentation of non-enhanced images. Together these results show that SalienceNet can be used as a common preprocessing step to automate nuclei segmentation pipelines for low-light microscopy images.

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“…Various computer-assisted approaches have been proposed for nuclei instance segmentation, ranging from conventional image processing techniques to classical machine learning and advanced deep learning-based approaches (10)(11)(12). Image processing techniques such as adaptive thresholding or watershed segmentaion are still widely used for non-sophisticated images.…”

Section: Introductionmentioning

confidence: 99%

A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images

et al. 2022

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Even in the era of precision medicine, with various molecular tests based on omics technologies available to improve the diagnosis process, microscopic analysis of images derived from stained tissue sections remains crucial for diagnostic and treatment decisions. Among other cellular features, both nuclei number and shape provide essential diagnostic information. With the advent of digital pathology and emerging computerized methods to analyze the digitized images, nuclei detection, their instance segmentation and classification can be performed automatically. These computerized methods support human experts and allow for faster and more objective image analysis. While methods ranging from conventional image processing techniques to machine learning-based algorithms have been proposed, supervised convolutional neural network (CNN)-based techniques have delivered the best results. In this paper, we propose a CNN-based dual decoder U-Net-based model to perform nuclei instance segmentation in hematoxylin and eosin (H&E)-stained histological images. While the encoder path of the model is developed to perform standard feature extraction, the two decoder heads are designed to predict the foreground and distance maps of all nuclei. The outputs of the two decoder branches are then merged through a watershed algorithm, followed by post-processing refinements to generate the final instance segmentation results. Moreover, to additionally perform nuclei classification, we develop an independent U-Net-based model to classify the nuclei predicted by the dual decoder model. When applied to three publicly available datasets, our method achieves excellent segmentation performance, leading to average panoptic quality values of 50.8%, 51.3%, and 62.1% for the CryoNuSeg, NuInsSeg, and MoNuSAC datasets, respectively. Moreover, our model is the top-ranked method in the MoNuSAC post-challenge leaderboard.

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Nucleus segmentation: towards automated solutions

Cited by 43 publications

References 86 publications

Annotation of Spatially Resolved Single-cell Data with STELLAR

Annotation of Spatially Resolved Single-cell Data with STELLAR

SalienceNet: an unsupervised Image-to-Image translation method for nuclei saliency enhancement in microscopy images

A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images

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