Combined Detection and Segmentation of Cell Nuclei in Microscopy Images Using Deep Learning

Ram, Sundaresh; Nguyen, Vicky T.; Limesand, Kirsten H.; Rodríguez, Jeffrey J.

doi:10.1109/ssiai49293.2020.9094614

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…This algorithm was also able to improve detection accuracy over methods such as Mask R-CNN and a previous network proposed by the authors [37]. Finally, a 3D Unet inspired segmentation network was combined with a small regression-based centroid detection branch and obtained an F1score of 82% in a high nuclei density 3D fluorescent image set, and 93% in a low nuclei density data set [38].…”

Section: Related Workmentioning

confidence: 87%

“…Although regression-based approaches have proven to be especially strong at cell-detection tasks in 2D images, few attempts have been made in 3D datasets. Additionally, when segmentation and detection approaches have been paired, the detection branches were usually composed of a few convolutional layers at most, and not similar to the highly accurate regression networks used in 2D [37], [38]. In this manuscript we propose a new segmentation-regression approach that can be applied in intact cardiac and brain tissues, which both include irregularly shaped and clustered nuclei.…”

Section: Related Workmentioning

confidence: 99%

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Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network

et al. 2021

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Advances in three-dimensional microscopy and tissue clearing are enabling whole-organ imaging with single-cell resolution. Fast and reliable image processing tools are needed to analyze the resulting image volumes, including automated cell detection, cell counting and cell analytics. Deep learning approaches have shown promising results in two-and three-dimensional nuclei detection tasks, however detecting overlapping or non-spherical nuclei of different sizes and shapes in the presence of a blurring point spread function remains challenging and often leads to incorrect nuclei merging and splitting. Here we present a new regression-based fully convolutional network that located a thousand nuclei centroids with high accuracy in under a minute when combined with V-net, a popular threedimensional semantic-segmentation architecture. High nuclei detection F1-scores of 95.3% and 92.5% were obtained in two different whole quail embryonic hearts, a tissue type difficult to segment because of its high cell density, and heterogeneous and elliptical nuclei. Similar high scores were obtained in the mouse brain stem, demonstrating that this approach is highly transferable to nuclei of different shapes and intensities. Finally, spatial statistics were performed on the resulting centroids. The spatial distribution of nuclei obtained by our approach most resembles the spatial distribution of manually identified nuclei, indicating that this approach could serve in future spatial analyses of cell organization.

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Section: Related Workmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network

et al. 2021

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“…We also suggest improving the adapted MMA algorithm by adding a frame differencing method as a pre-processing step, then exploiting better multi-scale feature selection to improve average F-score with reference to some of the latest models [35]- [44] on object detection and their post-processing schemes [45]. Furthermore, we are investigating some of the recent deep learning schemes [46]- [57] for detecting and tracking vehicles [27], [34], [39], [58]- [61] in accordance with the complexity analysis [45], [62]- [69] from the deep CNN-based multi-object detection and segmentation schemes [48]- [51], [53]- [56], [59]- [61], [64]- [66], [70]- [72] applied to wide-area aerial surveillance.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

et al. 2020

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Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The proposed scheme also performs spatial post-processing, which includes morphological opening and closing to shape and prune the detected objects, and temporal post-processing to further reduce false detections. We evaluate the performance of the proposed spatial processing on two local aerial video datasets and one parking vehicle dataset, and the performance of the proposed spatio-temporal processing scheme on five local aerial video datasets and one public dataset. Experimental evaluation shows that the proposed schemes improve vehicle detection performance for each of the nine algorithms when evaluated on seven datasets. Overall, the use of the proposed spatio-temporal processing scheme improves average F-score to above 0.8, achieves a 90% reduction in false positives and reduces percentage of wrong classifications to below 50% for some of the nine algorithms. INDEX TERMS Vehicle detection, hysteresis thresholding, spatio-temporal processing, wide-area aerial imagery.

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“…This is due to various reasons such as fluctuating intensities [4] , color change and morphological variations within structures of the cancer lesions in these images [5] , tumor heterogeneity [6] (see Fig. 1 ), low signal-to-noise-ratio [7] , [8] , variations in illumination [9] , microscopy imaging limitations [10] , [11] , [12] , [13] , and the large number of images and the number of lesions per image an expert has to demarcate. Moreover, the task of manual detection of cancer lesions on H&E slides can be subjective, leading to inter-observer variability.…”

Section: Introductionmentioning

confidence: 99%

Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network

Ram¹,

Tang²,

Bell³

et al. 2023

Neoplasia

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Combined Detection and Segmentation of Cell Nuclei in Microscopy Images Using Deep Learning

Cited by 5 publications

References 16 publications

Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network

Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network

Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network

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