Segmentation of Cell Images Based on Improved Deep Learning Approach

Huang, Chuanbo; Ding, Huali; Liu, Chuanling

doi:10.1109/access.2020.3001571

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…If the traditional FAST9-16 algorithm is used for detection, it meets the requirement that the gray value of more than 9 continuous pixels in the neighborhood of 16 pixels is sufficiently different. So, the system will identify it as a corner point, and the point p is only an edge point [13,14]. erefore, in order to exclude the interference of such edge points on the detection results, the FAST algorithm is improved as follows: 24-pixel points around the pixel point p are taken as the detection template, the gray value of the point p is I p , and a threshold T is set.…”

Section: 1mentioning

confidence: 99%

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Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

Min

Cheng

et al. 2022

Scientific Programming

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In order to improve the problems of poor accuracy and low efficiency in tobacco leaves disease recognition and diagnosis and avoid the misjudgment in tobacco disease recognition, a disease recognition and spot segmentation method based on the improved ORB algorithm was proposed. The improved FAST14-24 algorithm was used to preliminarily extract corners. It overcame the deficiency of the sensitivity of the traditional ORB corner detection algorithm to image edges. During the experiment, 28 parameters were obtained through the extraction of color features, morphological features, texture, and other features of tobacco disease spots. Through the experimental comparisons, it was found that the fitness of the improved ORB algorithm was 96.68 and the cross-checking rate was 93.21%. The validation and recognition rate for samples was 96%. The identification rate of tobacco brown spot disease and frog eye disease was 92%, and the identification rate of 6 categories in different periods was over 96%. The experimental results verified the effects of the disease identification fully.

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Section: 1mentioning

confidence: 99%

“…In formula (14), S(x, y) is the image of the object, R(x, y) is the reflection component of the object itself, and L(x, y) is the illumination component.…”

Section: Msrcrmentioning

confidence: 99%

Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

Min

Cheng

et al. 2022

Scientific Programming

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“…As future research, we plan to further improve the segmentation performance using a larger and on ImageNet pre-trained encoder or mixed convolution blocks [ 35 ], test-time augmentation [ 36 ], and the synthetic generation of new training samples [ 16 ]. In addition, studies about how cell features, e.g., size, shape and texture, influence the generalization ability to new cell types are needed.…”

Section: Discussionmentioning

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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“…In addition, a cascaded two-dimensional neural network with an intermediate statistical model for the segmentation of the knee meniscus is proposed, which is used to generate smaller patch input for the three-dimensional neural network model. The author introduces cytological analysis computational tools such as cell segmentation deep learning techniques capable of processing both free-floating and clumps of abnormal cells from digitised images of traditional Pap smears with a high overlapping rate, and cell image segmentation, in previous studies, no one proposed image segmentation for drug, diseased, and mitochondria cell, and some authors proposed image segmentation for medical, but no one proposed image segmentation for drug-treated image cell, diseased cell image, and mitochondria cell image, there is a gap in this area, that is why we work in this area, and we proposed a new algorithm for image segmentation, drug, diseased cell image, and mitochondria cell image [ 23 , 24 ]. More research is needed in this area, particularly in mitochondrial cells for measuring oxidative stress using machine learning.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

Nawabi

Sheng

Abbasi

et al. 2022

Oxidative Medicine and Cellular Longevity

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Most multicellular organisms require apoptosis, or programmed cell death, to function properly and survive. On the other hand, morphological and biochemical characteristics of apoptosis have remained remarkably consistent throughout evolution. Apoptosis is thought to have at least three functionally distinct phases: induction, effector, and execution. Recent studies have revealed that reactive oxygen species (ROS) and the oxidative stress could play an essential role in apoptosis. Advanced microscopic imaging techniques allow biologists to acquire an extensive amount of cell images within a matter of minutes which rule out the manual analysis of image data acquisition. The segmentation of cell images is often considered the cornerstone and central problem for image analysis. Currently, the issue of segmentation of mitochondrial cell images via deep learning receives increasing attention. The manual labeling of cell images is time-consuming and challenging to train a pro. As a courtesy method, mitochondrial cell imaging (MCI) is proposed to identify the normal, drug-treated, and diseased cells. Furthermore, cell movement (fission and fusion) is measured to evaluate disease risk. The newly proposed drug-treated, normal, and diseased image segmentation (DNDIS) algorithm can quickly segment mitochondrial cell images without supervision and further segment the highly drug-treated cells in the picture, i.e., normal, diseased, and drug-treated cells. The proposed method is based on the ResNet-50 deep learning algorithm. The dataset consists of 414 images mainly categorised into different sets (drug, diseased, and normal) used microscopically. The proposed automated segmentation method has outperformed and secured high precision (90%, 92%, and 94%); moreover, it also achieves proper training. This study will benefit medicines and diseased cell measurements in medical tests and clinical practices.

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Segmentation of Cell Images Based on Improved Deep Learning Approach

Cited by 12 publications

References 36 publications

Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

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

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

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