Abstract
Parasitic infections pose a significant health risk in many regions worldwide, requiring rapid and reliable diagnostic methods to identify and treat affected individuals. Recent advancements in deep learning have significantly improved the accuracy and efficiency of microscopic image analysis workflows, enabling its application in various domains such as medical diagnostics and microbiology. This work presents DT4PEIS, a novel two-stage architecture for the instance segmentation of parasite eggs in microscopic images. The first stage is a DEtection TRansformer (DETR) based architecture, which predicts the bounding boxes and class labels of the detected eggs. Then, the predicted bounding boxes are used as prompts to guide the segmentation process in the second stage, which is based on the Segment Anything Model (SAM) architecture. We evaluate the performance of the proposed method on the Chula-ParasiteEgg-11 dataset. Our results show that the proposed method outperforms the other architectures in terms of segmentation mean Average Precision (mAP), providing a more detailed and accurate representation of the detected eggs.
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The generated segmentation masks used in this work are publicly available [45].
References
Dogan N (2024) Intestinal parasites from past to present: taxonomy, paleoparasitology, geographic distribution, prevention and control strategies
Belizario V Jr, Ampo SAM, Henderson K, Santos T, Gerth-Guyette E, Guzman LM, Lumangaya C, Siao T, Chua RSCS (2024) Surveillance of soil-transmitted helminthiasis and schistosomiasis in the Philippines: review of current policies, guidelines and practices. Southeast Asian J Trop Med Public Health 55(4):177–195
Anantrasirichai N, Chalidabhongse TH, Palasuwan D, Naruenatthanaset K, Kobchaisawat T, Nunthanasup N, Boonpeng K, Ma X, Achim A (2022) ICIP 2022 challenge on parasitic egg detection and classification in microscopic images: dataset, methods and results. In: IEEE international conference on image processing (ICIP), IEEE pp 4306–4310
Suwannaphong T, Chavana S, Tongsom S, Palasuwan D, Chalidabhongse TH, Anantrasirichai N (2023) Parasitic egg detection and classification in low-cost microscopic images using transfer learning. SN Comput Sci 5(1):82
Capuozzo S, Marrone S, Gravina M, Cringoli G, Rinaldi L, Maurelli MP, Bosco A, Orrù G, Marcialis GL, Ghiani L et al (2024) Automating parasite egg detection: insights from the first ai-kfm challenge. Front Artif Intell 7:1325219
Chaibutr N, Pongpanitanont P, Laymanivong S, Thanchomnang T, Janwan P (2024) Development of a machine learning model for the classification of enterobius vermicularis egg. J Imaging 10(9):212
Gao Z, Huang J, Chen J, Shao T, Ni H, Cai H (2024) Deep transfer learning-based computer vision for real-time harvest period classification and impurity detection of porphyra haitnensis. Aquac Int 1–28
Thanchomnang T, Chaibutr N, Maleewong W, Janwan P (2024) Automatic detection of opisthorchis viverrini egg in stool examination using convolutional-based neural networks. PeerJ 12:16773
Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: Proceedings of european conference on computer vision (ECCV), Springer pp 213–229
Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W-Y, et al (2023) Segment anything. arXiv:2304.02643
Roberts LS, Janovy Jr J (2009) Gerald D. Schmidt & Larry S. Roberts’ Foundations of Parasitology. 8th edn. McGraw-Hill, New York
Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV) pp 2961–2969
Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) pp 6154–6162
Chen K, Pang J, Wang J, Xiong Y, Li X, Sun S, Feng W, Liu Z, Shi J, Ouyang W, et al (2019) Hybrid task cascade for instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) pp 4974–4983
Feng C, Zhong Y, Gao Y, Scott MR, Huang W (2021) TOOD: task-aligned one-stage object detection. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV), IEEE computer society pp 3490–3499
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) pp 770–778
Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) pp 1492–1500
Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A ConvNet for the 2020s. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) pp 11976–11986
Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, Xie S (2023) ConvNeXt V2: co-Designing and Scaling ConvNets With Masked Autoencoders. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) pp 16133–16142
Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature Pyramid Networks for Object Detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) pp 2117–2125
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV) pp 10012–10022
Liu S, Li F, Zhang H, Yang X, Qi X, Su H, Zhu J, Zhang L (2022) DAB-DETR: dynamic anchor boxes are better queries for DETR. arXiv:2201.12329
Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2021) Deformable DETR: deformable transformers for end-to-end object detection. In: International conference on learning representations (ICLR)
Zhang H, Li F, Liu S, Zhang L, Su H, Zhu J, Ni L, Shum H-Y (2023) DINO: DETR with improved denoising anchor boxes for end-to-end object detection. In: International conference on learning representations
Zong Z, Song G, Liu Y (2023) DETRs with collaborative hybrid assignments training. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV) pp 6748–6758
Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft COCO: common objects in context. In: Proceedings of european conference on computer vision (ECCV), Springer pp 740–755
Gupta A, Dollar P, Girshick R (2019) LVIS: a dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 5356–5364
Penpong N, Wanna Y, Kamjanlard C, Techasen A, Intharah T (2024) Attacking the out-of-domain problem of a parasite egg detection in-the-wild. Heliyon 10(4)
Kumar S, Arif T, Ahamad G, Chaudhary AA, Ali MA, Islam A (2024) Improving faster r-cnn generalization for intestinal parasite detection using cycle-gan based data augmentation. Disc Appl Sci 6(5):1–13
Rajasekar SJS, Jaswal G, Perumal V, Ravi S, Dutt V (2023) Parasite. ai–an automated parasitic egg detection model from microscopic images of fecal smears using deep learning techniques. In: 2023 International conference on advances in computing, communication and applied informatics (ACCAI), IEEE pp 1–9
Aung ZH, Srithaworn K, Achakulvisut T (2022) Multitask learning via pseudo-label generation and ensemble prediction for parasitic egg cell detection: IEEE ICIP challenge 2022. In: IEEE international conference on image processing (ICIP), IEEE pp 4273–4277
Jocher G (2020) YOLOv5 by Ultralytics. https://github.com/ultralytics/yolov5
Kumar S, Arif T, Ahamad G, Chaudhary AA, Khan S, Ali MA (2023) An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5. Diagnostics 13(18):2978
Liang T, Chu X, Liu Y, Wang Y, Tang Z, Chu W, Chen J, Ling H (2022) CBNet: a composite backbone network architecture for object detection. IEEE Trans Image Process 31:6893–6906
Wan Z, Liu S, Ding F, Li M, Srivastava G, Yu K (2023) C2BNet: a deep learning architecture with coupled composite backbone for parasitic EGG detection in microscopic images. IEEE J Biomed Health Inform
AlDahoul N, Karim HA, Momo MA, Escobar FIF, Magallanes VA, Tan MJT (2023) Parasitic egg recognition using convolution and attention network. Sci Rep 13(1):14475
Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2020) Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) pp 9759–9768
Tian Z, Shen C, Chen H, He T (2019) FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV) pp 9627–9636
Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV) pp 2980–2988
Zamami R, Kohagura K, Kinjyo K, Nakamura T, Kinjo T, Yamazato M, Ishida A, Ohya Y (2021) The association between glomerular diameter and secondary focal segmental glomerulosclerosis in chronic kidney disease. Kidney Blood Press Res 46(4):433–440
He K, Chen X, Xie S, Li Y, Dollár P, Girshick R (2022) Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) pp 16000–16009
Palasuwan D, Naruenatthanaset K, Kobchaisawat T, Chalidabhongse TH, Nunthanasup N, Boonpeng K, Anantrasirichai N (2022) Parasitic egg detection and classification in microscopic images. IEEE Dataport
Ruiz-Santaquiteria J, Muñoz J, Pedraza A, Deniz O, Bueno G (2024) DT4PEIS masks repository. Mendeley data. https://doi.org/10.17632/d3wt5ynm7n.1
MMDetection Contributors (2018) OpenMMLab detection toolbox and benchmark. https://github.com/open-mmlab/mmdetection
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin-transformer-object-detection. https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/
Liang T, Chu X, Liu Y, Wang Y, Tang Z, Chu W, Chen J, Ling H (2022) CBNetV2. https://github.com/VDIGPKU/CBNetV2
Ge Z, Liu S, Wang F, Li Z, Sun J (2021) YOLOX: exceeding YOLO series in 2021. arXiv:2107.08430
Bochkovskiy A, Wang C-Y, Liao H-YM (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv:2004.10934
Padilla R, Netto SL, Da Silva EA (2020) A survey on performance metrics for object-detection algorithms. In: 2020 International conference on systems, signals and image processing (IWSSIP), IEEE pp 237–242
Acknowledgements
This work has been funded by the DIAMOND project (Ref. TED2021-132147B-100), the HANS project (Ref. PID2021-127567NB-I00), both projects supported by the Spanish Ministry of Science, Innovation, and Universities, and by the European Union NextGenerationEU/PRTR. The work has also been partially funded by the ARTE project (Ref. 2022-GRIN-34352) supported by University of Castilla-La Mancha.
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All authors conceived the idea and designed the experiments. J.R. implemented the experiments and summarized the results. J.R. and A.P. wrote the manuscript. O.D. and G.B. supervised the project. All authors reviewed the manuscript.
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Ruiz-Santaquiteria, J., Pedraza, A., Deniz, O. et al. DT4PEIS: detection transformers for parasitic egg instance segmentation. Appl Intell 55, 271 (2025). https://doi.org/10.1007/s10489-024-06199-y
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DOI: https://doi.org/10.1007/s10489-024-06199-y