Abstract
Trichomoniasis is a common infectious disease with high incidence caused by the parasite Trichomonas vaginalis, increasing the risk of getting HIV in humans if left untreated. Automated detection of Trichomonas vaginalis from microscopic images can provide vital information for diagnosis of trichomoniasis. However, accurate Trichomonas vaginalis segmentation (TVS) is a challenging task due to the high appearance similarity between the Trichomonas and other cells (e.g., leukocyte), the large appearance variation caused by their motility, and, most importantly, the lack of large-scale annotated data for deep model training. To address these challenges, we elaborately collected the first large-scale Microscopic Image dataset of Trichomonas Vaginalis, named TVMI3K, which consists of 3,158 images covering Trichomonas of various appearances in diverse backgrounds, with high-quality annotations including object-level mask labels, object boundaries, and challenging attributes. Besides, we propose a simple yet effective baseline, termed TVNet, to automatically segment Trichomonas from microscopic images, including high-resolution fusion and foreground-background attention modules. Extensive experiments demonstrate that our model achieves superior segmentation performance and outperforms various cutting-edge object detection models both quantitatively and qualitatively, making it a promising framework to promote future research in TVS tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Brandao, P., Mazomenos, E., Ciuti, G., Caliò, R., Bianchi, F., Menciassi, A., et al.: Fully convolutional neural networks for polyp segmentation in colonoscopy. In: Medical Imaging: Computer-Aided Diagnosis. vol. 10134, pp. 101–107 (2017)
Chen, L., et al.: SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: IEEE CVPR, pp. 5659–5667 (2017)
Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: IEEE ICCV, pp. 4548–4557 (2017)
Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. In: IJCAI. pp. 698–704 (2018)
Fan, D.P., Ji, G.P., Cheng, M.M., Shao, L.: Concealed object detection. IEEE TPAMI, pp. 1 (2021)
Fan, D.P., Ji, G.P., Sun, G., Cheng, M.M., Shen, J., Shao, L.: Camouflaged object detection. In: IEEE CVPR, pp. 2777–2787 (2020)
Fan, D.P., et al.: Pranet: parallel reverse attention network for polyp segmentation. In: MICCAI, pp. 263–273 (2020)
Fan, D.P., Zhou, T., Ji, G.P., Zhou, Y., Chen, G., Fu, H., Shen, J., Shao, L.: INF-NET: Automatic covid-19 lung infection segmentation from CT images. IEEE TMI 39(8), 2626–2637 (2020)
Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: Res2net: A new multi-scale backbone architecture. IEEE TPAMI 43(2), 652–662 (2019)
Harp, D.F., Chowdhury, I.: Trichomoniasis: evaluation to execution. Eur. J. Obstet. Gynecol. Reprod. Biol. 157(1), 3–9 (2011)
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582–596 (2019)
Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.W., Wu, J.: Unet 3+: A full-scale connected unet for medical image segmentation. In: ICASSP. pp. 1055–1059 (2020)
Ji, G.-P., Chou, Y.-C., Fan, D.-P., Chen, G., Fu, H., Jha, D., Shao, L.: Progressively normalized self-attention network for video polyp segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. Progressively normalized self-attention network for video polyp segmentation, vol. 12901, pp. 142–152. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_14
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Li, D., et al.: Robust blood cell image segmentation method based on neural ordinary differential equations. In: Computational and Mathematical Methods in Medicine 2021 (2021)
Li, J., et al.: A systematic collection of medical image datasets for deep learning. arXiv preprint arXiv:2106.12864 (2021)
Li, L., Liu, J., Yu, F., Wang, X., Xiang, T.Z.: Mvdi25k: A large-scale dataset of microscopic vaginal discharge images. BenchCouncil Transactions on Benchmarks, Standards and Evaluations 1(1), 100008 (2021)
Liu, J., Dong, B., Wang, S., Cui, H., Fan, D.P., Ma, J., Chen, G.: Covid-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework. Med. Image Anal. 74, 102205 (2021)
Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps? In: IEEE CVPR. pp. 248–255 (2014)
Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: IEEE CVPR, pp. 733–740. IEEE (2012)
Qin, X., Zhang, Z., Huang, C., Dehghan, M., et al.: U2-net: going deeper with nested u-structure for salient object detection. Pattern Recogn. 106, 107404 (2020)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. U-net: Convolutional networks for biomedical image segmentation, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Siddique, N., Paheding, S., Elkin, C.P., Devabhaktuni, V.: U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access, pp. 82031–82057 (2021)
Sun, P., Zhang, W., Wang, H., Li, S., Li, X.: Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion. In: IEEE CVPR, pp. 1407–1417 (2021)
Tang, W., Zou, D., Yang, S., Shi, J., Dan, J., Song, G.: A two-stage approach for automatic liver segmentation with faster R-CNN and deeplab. Neural Comput. Appl. 32(11), 6769–6778 (2020)
Vos, T., Allen, C., Arora, M., Barber, R.M., Bhutta, Z.A., Brown, A., et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the global burden of disease study 2015. The Lancet 388(10053), 1545–1602 (2016)
Wang, X., Du, X., Liu, L., Ni, G., Zhang, J., Liu, J., Liu, Y.: Trichomonas vaginalis detection using two convolutional neural networks with encoder-decoder architecture. Appl. Sci. 11(6), 2738 (2021)
Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: MICCAI. pp. 699–708 (2021)
Wei, J., Wang, S., Huang, Q.: F\(^3\)net: fusion, feedback and focus for salient object detection. In: AAAI, pp. 12321–12328 (2020)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: ECCV. pp. 3–19 (2018)
Workowski, K.A.: Sexually transmitted infections and HIV: diagnosis and treatment. Topics Antiviral Med. 20(1), 11 (2012)
Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: IEEE CVPR, pp. 3907–3916 (2019)
Wu, Z., Su, L., Huang, Q.: Stacked cross refinement network for edge-aware salient object detection. In: IEEE ICCV, pp. 7263–7272 (2019)
Zhang, Y., et al.: A multi-branch hybrid transformer network for corneal endothelial cell segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 99–108. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_10
Zhao, X., Wu, Y., Song, G., Li, Z., et al.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2018)
Zhao, X., Zhang, L., Lu, H.: Automatic polyp segmentation via multi-scale subtraction network. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 120–130. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_12
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: DLMIA, pp. 3–11 (2018)
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE TMI, pp. 1856–1867 (2019)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, L., Liu, J., Wang, S., Wang, X., Xiang, TZ. (2022). Trichomonas Vaginalis Segmentation in Microscope Images. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-16440-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16439-2
Online ISBN: 978-3-031-16440-8
eBook Packages: Computer ScienceComputer Science (R0)