Segmenting and tracking cell instances with cosine embeddings and recurrent hourglass networks

Payer, Christian; Štern, Darko; Feiner, Marlies; Bischof, Horst; Urschler, Martin

doi:10.1016/j.media.2019.06.015

Cited by 55 publications

(80 citation statements)

References 38 publications

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“…The choice of loss function is a crucial component for better segmenting the dense cell regions. It is concurred in the recent work of mitosis event detection, who found the network with combined embedding loss functions performed better than the commonly used loss functions 29 . Our experimental results of balanced network trained using combined RWCDL function outperformed the other designed networks, especially in localizing regions with lower number of instances.…”

Section: Discussionsupporting

confidence: 78%

Prediction of Sequential Organelles Localization under Imbalance using A Balanced Deep U-Net

Yudistira

Kavitha

Itabashi

et al. 2020

Sci Rep

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Assessing the structure and function of organelles in living organisms of the primitive unicellular red algae Cyanidioschyzon merolae on three-dimensional sequential images demands a reliable automated technique in the class imbalance among various cellular structures during mitosis. Existing classification networks with commonly used loss functions were focused on larger numbers of cellular structures that lead to the unreliability of the system. Hence, we proposed a balanced deep regularized weighted compound dice loss (RWCDL) network for better localization of cell organelles. Specifically, we introduced two new loss functions, namely compound dice (CD) and RWCD by implementing multi-class variant dice and weighting mechanism, respectively for maximizing weights of peroxisome and nucleus among five classes as the main contribution of this study. We extended the Unet-like convolution neural network (CNN) architecture for evaluating the ability of our proposed loss functions for improved segmentation. The feasibility of the proposed approach is confirmed with three different large scale mitotic cycle data set with different number of occurrences of cell organelles. In addition, we compared the training behavior of our designed architectures with the ground truth segmentation using various performance measures. The proposed balanced RWCDL network generated the highest area under the curve (AUC) value in elevating the small and obscure peroxisome and nucleus, which is 30% higher than the network with commonly used mean square error (MSE) and dice loss (DL) functions. The experimental results indicated that the proposed approach can efficiently identify the cellular structures, even when the contour between the cells is obscure and thus convinced that the balanced deep RWCDL approach is reliable and can be helpful for biologist to accurately identify the relationship between the cell behavior and structures of cell organelles during mitosis.

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Section: Discussionsupporting

confidence: 78%

Prediction of Sequential Organelles Localization under Imbalance using A Balanced Deep U-Net

Yudistira

Kavitha

Itabashi

et al. 2020

Sci Rep

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“…Deep learning based segmentation pipelines for specific purposes in bioimaging a. Deep learning for cell segmentation and tracking: Deep learning pipelines for instance segmentation coupled with cell tracking have been proposed in several works, such as (Payer, Štern, Feiner, Bischof, & Urschler, 2019) where the pipeline makes predictions for every cell instance in videos, as well produces temporally connected instance segmentations. Cell instance segmentation in calcium imaging videos is described in (Kirschbaum, Bailoni, & Hamprecht, 2020) which uses temporal information to estimate pixel-wise correlation and shape information to identify cells and classify active and non-active cells.…”

Section: Acknowledgementmentioning

confidence: 99%

Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets

Kar

Petit

Refahi

et al. 2021

Preprint

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Segmenting three dimensional microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state-of-the-art for image segmentation problems. However, it remains difficult to define their relative performance as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artefacts. A new method for segmentation quality evaluation was adopted which isolates segmentation errors due to under/over segmentation. This is complemented with new visualisation strategies that make interactive exploration of segmentation quality possible. Our analysis shows that the DL pipelines have very different levels of accuracy. Two of them show high performance, and offer clear advantages in terms of adaptability to new data.

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“…Although deep learning methods have been successfully applied to multi object tracking on natural images [17,18], there are only few deep learning approaches for cell tracking [19,20]. In [19], cells are simultaneously segmented and tracked by combining a recurrent hourglass network with a pixel-wise metric embedding learning. [20] proposes a particle-filter-based motion model in combination with a CNN-based observation model.…”

Section: Introductionmentioning

confidence: 99%

Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy

et al. 2020

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The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a challenging problem. In this paper, we present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. Furthermore, this representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types. For the prediction of the proposed neighbor distances, an adapted U-Net convolutional neural network (CNN) with two decoder paths is used. In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking. The adapted tracking algorithm includes a movement estimation in the cost function to re-link tracks with missing segmentation masks over a short sequence of frames. Our combined tracking by detection method has proven its potential in the IEEE ISBI 2020 Cell Tracking Challenge (http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE multiple top three rankings including two top performances using a single segmentation model for the diverse data sets.

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Segmenting and tracking cell instances with cosine embeddings and recurrent hourglass networks

Cited by 55 publications

References 38 publications

Prediction of Sequential Organelles Localization under Imbalance using A Balanced Deep U-Net

Prediction of Sequential Organelles Localization under Imbalance using A Balanced Deep U-Net

Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets

Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy

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