Identifying rumen protozoa in microscopic images of ruminant with improved YOLACT instance segmentation

Shang, Zhenni; Wang, Xiangnan; Jiang, Yu; Li, Zongjun; Ning, Jifeng

doi:10.1016/j.biosystemseng.2022.01.005

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…These improvements effectively enhance the segmentation accuracy of the railroad track and can accurately and efficiently segment the railroad track line components. Zhenni Shang [28] used Yolact, which introduces the SE attention mechanism to enhance feature expression and FRelu activation function, for efficient segmentation of protozoa in microscopic images.…”

Section: Related Workmentioning

confidence: 99%

Instance Segmentation Method based on R2SC-Yolact++

Ma,

Cai,

Xie

et al. 2023

IJACSA

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To address the problems of missed detection, segmentation error and poor target edge segmentation in the instance segmentation model, a R2SC-Yolact++ instance segmentation approach based on the improved Yolact++ is proposed. Firstly, the backbone network adopts Res2Net which introduces spatial attention mechanism (SAM) to improve the problem of segmentation error by better extracting feature information; then, high-quality masks are obtained by fusing the detail information of the shallow feature P2 as the input to the prototype mask branch; finally, the problem of missed detection was solved by introducing Cluster-NMS in order to improve the accuracy of the detection boxes. In order to illustrate the effectiveness of the improved model, experiments were conducted on two publicly available datasets, the COCO and CVPPP datasets. The experimental results show that the accuracy on the COCO dataset is 1.1% higher than the original model. And the accuracy on the CVPPP dataset is 1.7% better than before the improvement, which is better than other mainstream instance segmentation algorithms such as Mask RCNN. Finally, the improved model is applied to the insulator dataset, which can segment the shed of insulator accurately.

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Section: Related Workmentioning

confidence: 99%

Instance Segmentation Method based on R2SC-Yolact++

Ma,

Cai,

Xie

et al. 2023

IJACSA

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“…The utility of automatic cell segmentation approaches significantly enhances the efficiency of ophthalmologists, thereby reducing the dependency on highly experienced experts (Shang et al, 2022 ). Various widely employed algorithms, including K-means clustering (Yan et al, 2012 ), edge detection (Pan et al, 2015 ), and watershed (Sharif et al, 2012 ) have been utilized to achieve automatic cell segmentation.…”

Section: Introductionmentioning

confidence: 99%

U-NTCA: nnUNet and nested transformer with channel attention for corneal cell segmentation

Zhang,

et al. 2024

Front. Neurosci.

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BackgroundAutomatic segmentation of corneal stromal cells can assist ophthalmologists to detect abnormal morphology in confocal microscopy images, thereby assessing the virus infection or conical mutation of corneas, and avoiding irreversible pathological damage. However, the corneal stromal cells often suffer from uneven illumination and disordered vascular occlusion, resulting in inaccurate segmentation.MethodsIn response to these challenges, this study proposes a novel approach: a nnUNet and nested Transformer-based network integrated with dual high-order channel attention, named U-NTCA. Unlike nnUNet, this architecture allows for the recursive transmission of crucial contextual features and direct interaction of features across layers to improve the accuracy of cell recognition in low-quality regions. The proposed methodology involves multiple steps. Firstly, three underlying features with the same channel number are sent into an attention channel named gnConv to facilitate higher-order interaction of local context. Secondly, we leverage different layers in U-Net to integrate Transformer nested with gnConv, and concatenate multiple Transformers to transmit multi-scale features in a bottom-up manner. We encode the downsampling features, corresponding upsampling features, and low-level feature information transmitted from lower layers to model potential correlations between features of varying sizes and resolutions. These multi-scale features play a pivotal role in refining the position information and morphological details of the current layer through recursive transmission.ResultsExperimental results on a clinical dataset including 136 images show that the proposed method achieves competitive performance with a Dice score of 82.72% and an AUC (Area Under Curve) of 90.92%, which are higher than the performance of nnUNet.ConclusionThe experimental results indicate that our model provides a cost-effective and high-precision segmentation solution for corneal stromal cells, particularly in challenging image scenarios.

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“…The advantages of protozoa in water environment assessment and monitoring strongly support their widespread application in various water bodies. The automatic analysis of protozoa in different environments poses a challenging problem with a significant impact on ecosystem assessment [3], [4]. To efficiently assess protozoa in water samples, many researchers have focused on the development of automatic tools based on computer vision and machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

PR-YOLO: Improved YOLO for fast protozoa classification and segmentation

Yang

Chen

et al. 2023

Preprint

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Protozoa, such as Ceratium and Paramecium, play a fundamental role in establishing sustainable ecosystems. The distribution and classification of certain protozoa and their species are informative indicators to evaluate environmental quality. However, protozoa analysis is traditionally performed by molecular biological (DNA, RNA) or morphological methods, which are time-consuming and require an experienced laboratory operator. In this work, we adopt a deep learning-based network to solve the protozoa classification task. This method utilizes microscope images to help researchers analyse the protozoa population and species, reducing the cost of experimental sample storage and relieving the burden on laboratory operators. However, the shape and size of protozoa vary greatly, which places a great burden on the optimization of DCNN feature distillation. It is a great challenge to build a fast and precise protozoa analysis image. We present a new version of YOLO-v5 with better performance and extend it with instance segmentation called PR-YOLO. Building on the original YOLOv5, we added two extra parallel branches to PR-YOLO, which perform different segmentation subtasks: (1) a branch generates a set of prototype masks (images); (2) the other branch predicts a set of mask coefficients corresponding to prototype masks for each instance mask generation. Then, to improve the classification accuracy, we introduced transformer encoding blocks and lightweight Convolution Block Attention Modules (CBAMs) to explore the prediction potential with a self-attention mechanism. To quantitatively evaluate the performance of PR-YOLO, a comprehensive experiment was carried out on the hand-segmented microscopic protozoa images. Our model obtained the best results, with average classification accuracy of 96.83% and mean Average Precision(mAP) of 86.92% with a speed of 25.2 fps, which proves that the method has high robustness in this application field.

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Identifying rumen protozoa in microscopic images of ruminant with improved YOLACT instance segmentation

Cited by 6 publications

References 14 publications

Instance Segmentation Method based on R2SC-Yolact++

Instance Segmentation Method based on R2SC-Yolact++

U-NTCA: nnUNet and nested transformer with channel attention for corneal cell segmentation

PR-YOLO: Improved YOLO for fast protozoa classification and segmentation

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