DeepCIS: An end-to-end Pipeline for Cell-type aware Instance Segmentation in Microscopic Images

Khalid, Nabeel; Munir, Mohsin; Edlund, Christoffer; Jackson, Timothy R.; Trygg, Johan; Sjögren, Rickard; Dengel, Andreas; Ahmed, Sheraz

doi:10.1109/bhi50953.2021.9508480

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…Edlund et al (2021) proposed anchor-free and anchorbased pipelines for the cell segmentation using the LIVECell dataset [3]. [8] proposed a pipeline to perform cell-type aware segmentation in microscopic images using the EVICAN dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Studying cell migration, cell count, cell proliferation, cell morphology, cellular interactions, and cellular events like cell death are all possible with adequate cell segmentation. Deep learning approaches for instance cell segmentation [3,7,8,16,17,19,20] are showing promising results, but they rely heavily on precise full mask supervision for training. Manually annotating a groundtruth mask for each cell is a very labor-intensive, expensive, complex, and time-consuming task.…”

Section: Introductionmentioning

confidence: 99%

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Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation

Khalid

Schmeisser

Koochali

et al. 2022

Lecture Notes in Computer Science

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Identifying cells in microscopic images is a crucial step toward studying image-based cell biology research. Cell instance segmentation provides an opportunity to study the shape, structure, form, and size of cells. Deep learning approaches for cell instance segmentation rely on the instance segmentation mask for each cell, which is a labor-intensive and expensive task. An ample amount of unlabeled microscopic data is available in the cell biology domain, but due to the tedious and exorbitant nature of the annotations needed for the cell instance segmentation approaches, the full potential of the data is not explored. This paper presents a weakly supervised approach, which can perform cell instance segmentation by using only point and bounding box-based annotation. This enormously reduces the annotation efforts. The proposed approach is evaluated on a benchmark dataset i.e., LIVECell, whereby only using a bounding box and randomly generated points on each cell, it achieved the mean average precision score of 43.53% which is as good as the full supervised segmentation method trained with complete segmentation mask. In addition, it is 3.71 times faster to annotate with a bounding box and point in comparison to full mask annotation.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation

Khalid

Schmeisser

Koochali

et al. 2022

Lecture Notes in Computer Science

Self Cite

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show abstract

“…There are many available studies addressing detection and segmentation of cell and nucleus using traditional computer vision algorithms [7]- [10] and deep learning-based approaches [3]- [5], [11]- [17]. Traditional computer vision approaches use procedures like intensity thresholding, region accumulation, and deformable model fitting [18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Unfortunately, there are very few approaches that address the cell-type aware segmentation in a monoculture environment. [11] proposed a pipeline for celltype aware instance segmentation in monoculture microscopic images using the EVICAN60 [5]. The author also reported several limitations of the EVICAN60 dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The EVICAN60 dataset contains classes for 30 cell cultures. EVICAN60 dataset is partially annotated and the instances per class are very low [11]. LIVECell dataset, on the other hand, consists of 5,239 manually annotated, expertvalidated, phase contrast microscopy images with a total of 1,686,352 individual cells annotated from eight different cell cultures.…”

Section: Datasetmentioning

confidence: 99%

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DeepMuCS: A Framework for Mono- & Co-culture Microscopic Image Analysis: From Generation to Segmentation

Khalid¹,

Koochali²,

Rajashekhar³

et al. 2022

Preprint

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<div>Discrimination between cell types in the co-culture environment with multiple cell lines can assist in examining the interaction between different cell populations. Identifying different cell cultures along with segmentation in co-culture is essential for understanding the cellular mechanisms associated with disease states. Extracting the information from the co-culture models can help in quantifying the sub-population response to treatment conditions. In the past, there exists minimal progress related to cell-type aware segmentation in the monoculture and no development whatsoever for the co-culture. The introduction of the LIVECell dataset has provided us with the opportunity to perform experiments for cell-type aware segmentation. However, it is composed of microscopic images in a monoculture environment. In this paper, we have proposed a pipeline for coculture microscopic images data generation, where each image can contain multiple cell cultures. In addition, we have proposed a pipeline for culture-dependent cell segmentation in monoculture and co-culture microscopic images. Based on extensive evaluation, it was revealed that it is possible to achieve good quality cell-type aware segmentation in mono- and co-culture microscopic images.</div>

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PACE: Point Annotation-Based Cell Segmentation for Efficient Microscopic Image Analysis

Khalid,

Froes,

Caroprese

et al. 2023

Lecture Notes in Computer Science

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Cells are essential to life because they provide the functional, genetic, and communication mechanisms essential for the proper functioning of living organisms. Cell segmentation is pivotal for any biological hypothesis validation/analysis i.e., to get valuable insights into cell behavior, function, diagnosis, and treatment. Deep learning-based segmentation methods have high segmentation precision, however, need fully annotated segmentation masks for each cell annotated manually by the experts, which is very laborious and costly. Many approaches have been developed in the past to reduce the effort required to annotate the data manually and even though these approaches produce good results, there is still a noticeable difference in performance when compared to fully supervised methods. To fill that gap, a weakly supervised approach, PACE, is presented, which uses only the point annotations and the bounding box for each cell to perform cell instance segmentation. The proposed approach not only achieves 99.8% of the fully supervised performance, but it also surpasses the previous state-of-the-art by a margin of more than 4%.

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DeepCIS: An end-to-end Pipeline for Cell-type aware Instance Segmentation in Microscopic Images

Cited by 7 publications

References 6 publications

Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation

Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation

DeepMuCS: A Framework for Mono- & Co-culture Microscopic Image Analysis: From Generation to Segmentation

PACE: Point Annotation-Based Cell Segmentation for Efficient Microscopic Image Analysis

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