Object detection for automatic cancer cell counting in zebrafish xenografts

Albuquerque, Carina; Vanneschi, Leonardo; Henriques, Roberto; Castelli, Mauro; Póvoa, Vanda; Fior, Rita; Papanikolaou, Nikolaos

doi:10.1371/journal.pone.0260609

Cited by 10 publications

(4 citation statements)

References 47 publications

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“…At last, we evaluate SAT on object detection and identify images of bicycle and electric bicycle. According to our training protocol, SAT model appears perfect performance, such as mAP (Albuquerque et al,2021) and maxDets (Shermin et al,2021). The Average Precision (AP) reaches 95%, the small area metric reaches 90%, the medium area metric reaches 94.8% and the large area metric reaches 94.5%, as shown in Table 4.…”

Section: Object Detectionmentioning

confidence: 84%

An Enhanced Light Object Detection for Indiscernible Object in the Special Scene

Zhang

Feng²,

Wang³

et al. 2022

JER is an international, peer-reviewed journal that publishes f

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We present a transfer learning method named Special Application Transfer (SAT) for special object detection in a real life scenario. Our method improves fine-tuning hyper-parameter and adds unrecognized samples to detect special samples when training object detection neural networks for classification. We implement the model of NanoDet on special supervised datasets and fine-tune the hyper-parameter on a target task. More importantly, we combine a few carefully selected samples in training and simple heuristic fine-tuning to achieve good performance on special object detection in real-life scenarios. Our method (SAT) performs well across surprisingly the small dataset the medium dataset and the large dataset. SAT achieves 95% AP (Average Precision) on the small dataset, 94.8% AP on the medium dataset, and 94.5% AP on the large dataset. The performances of AP run-time and training convergence are perfect, compared with the original method and well-established famous methods on the challenging COCO dataset and our dataset. We hope our work could promote and complete the practical application in more real life scenarios.

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Section: Object Detectionmentioning

confidence: 84%

An Enhanced Light Object Detection for Indiscernible Object in the Special Scene

Zhang

Feng²,

Wang³

et al. 2022

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

show abstract

“…Upon comparing with the existing literature, we found that our development was empirically performing better, in terms of mAP and accuracy. Similar models were deployed for cancer cell counting and detection in zebrafish xenografts 29 . The study mentioned that the entirely fine-tuned model was able to achieve an mAP of 0.8457, considering all the background noise in the images and avoiding all the unwanted small artifacts in the images.…”

Section: Discussionmentioning

confidence: 99%

Automated detection of patterned single-cells within hydrogel using deep learning

Debnath

Hattori

Okamoto

et al. 2022

Sci Rep

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Single-cell analysis has been widely used in various biomedical engineering applications, ranging from cancer diagnostics, and immune response monitoring to drug screening. Single-cell isolation is fundamental for observing single-cell activities and an automatic finding method of accurate and reliable cell detection with few possible human errors is also essential. This paper reports trapping single cells into photo patternable hydrogel microwell arrays and isolating them. Additionally, we present an object detection-based DL algorithm that detects single cells in microwell arrays and predicts the presence of cells in resource-limited environments at the highest possible mAP (mean average precision) of 0.989 with an average inference time of 0.06 s. This algorithm leads to the enhancement of the high-throughput single-cell analysis, establishing high detection precision and reduced experimentation time.

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“…A DL-based cell classification and counting system were created using the inception (ResNet V2) feature extractor, a highly tuned architecture based on the FR-CNN. Compared to conventional Faster R-CNN, their technique increased the average precision of the tested data set from 71% to 85% ( Albuquerque et al, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

A deep learning approach for the detection and counting of colon cancer cells (HT-29 cells) bunches and impurities

Haq,

Mazhar,

Naz Asif

et al. 2023

PeerJ Computer Science

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HT-29 has an epithelial appearance as a human colorectal cancer cell line. Early detection of colorectal cancer can enhance survival rates. This study aims to detect and count HT-29 cells using a deep-learning approach (ResNet-50). The cell lines were procured from Procell Life Science & Technology Co., Ltd. (Wuhan, China). Further, the dataset is self-prepared in lab experiments, cell culture, and collected 566 images. These images contain two classes; the HT-29 human colorectal adenocarcinoma cells (blue shapes in bunches) and impurities (tinny circular grey shapes). These images are annotated with the help of an image labeller as impurity and cancer cells. Then afterwards, the images are trained, validated, and tested against the deep learning approach ResNet50. Finally, in each image, the number of impurity and cancer cells are counted to find the accuracy of the proposed model. Accuracy and computational expense are used to gauge the network’s performance. Each model is tested ten times with a non-overlapping train and random test splits. The effect of data pre-processing is also examined and shown in several tasks. The results show an accuracy of 95.5% during training and 95.3% in validation for detecting and counting HT-29 cells. HT-29 cell detection and counting using deep learning is novel due to the scarcity of research in this area, the application of deep learning, and potential performance improvements over traditional methods. By addressing a gap in the literature, employing a unique dataset, and using custom model architecture, this approach contributes to advancing colon cancer understanding and diagnosis techniques.

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Object detection for automatic cancer cell counting in zebrafish xenografts

Cited by 10 publications

References 47 publications

An Enhanced Light Object Detection for Indiscernible Object in the Special Scene

An Enhanced Light Object Detection for Indiscernible Object in the Special Scene

Automated detection of patterned single-cells within hydrogel using deep learning

A deep learning approach for the detection and counting of colon cancer cells (HT-29 cells) bunches and impurities

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