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Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue

Published: 01 July 2024 Publication History

Abstract

Background:

Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model’s viability and applicability in the diagnostic process. A deeper understanding of predictions is necessary in order to support histopathologists.

Methods:

We propose a method for providing supporting information in the form of segmentation of histological structures to histopathologists based on these principles. The proposed method utilizes nuclei type and density information in addition to standard image input provided at two different zoom levels for the semantic segmentation of blood vessels, inflammation, and endocardium in heart tissue.

Results:

The proposed method was able to reach state-of-the-art segmentation results. The overall quality and viability of the predictions was qualitatively evaluated by two pathologists and a histotechnologist.

Conclusions:

The decision process of the proposed deep learning model utilizes the provided information sources correctly and simulates the decision process of histopathologists via the usage of a custom-designed attention gate that provides a combination of spatial and encoder attention mechanisms. The implementation is available at https://github.com/mathali/IEDL-segmentation-of-heart-tissue.

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Highlights

Deep learning system that helps diagnose rejection risk in heart transplant patients.
System segments endocardium, blood vessels, and inflammation in histological samples.
Custom attention gate in architecture mirrors pathologist decision-making.

References

[1]
Srinidhi C.L., Ciga O., Martel A.L., Deep neural network models for computational histopathology: A survey, Med. Image Anal. 67 (2021),.
[2]
Eccher A., Pagni F., Marletta S., Munari E., Dei Tos A.P., Perspective of a pathologist on benchmark strategies for artificial intelligence development in organ transplantation, Crit. Rev. Oncog. 28 (3) (2023),.
[3]
Jimenez-Coll V., Llorente S., Boix F., Alfaro R., Galián J.A., Martinez-Banaclocha H., Botella C., Moya-Quiles M.R., Muro-Pérez M., Minguela A., et al., Monitoring of serological, cellular and genomic biomarkers in transplantation, computational prediction models and role of cell-free DNA in transplant outcome, Int. J. Mol. Sci. 24 (4) (2023) 3908,.
[4]
Benjamin E.J., Virani S.S., Callaway C.W., Chamberlain A.M., Chang A.R., Cheng S., Chiuve S.E., Cushman M., Delling F.N., Deo R., et al., Heart disease and stroke statistics—2018 update: a report from the American heart association, Circulation 137 (12) (2018) e67–e492,.
[5]
Ziaeian B., Fonarow G.C., Epidemiology and aetiology of heart failure, Nat. Rev. Cardiol. 13 (6) (2016) 368–378,.
[6]
Tong L., Hoffman R., Deshpande S.R., Wang M.D., Predicting heart rejection using histopathological whole-slide imaging and deep neural network with dropout, in: 2017 IEEE EMBS International Conference on Biomedical & Health Informatics, BHI, IEEE, 2017, pp. 1–4,.
[7]
Peyster E.G., Arabyarmohammadi S., Janowczyk A., Azarianpour-Esfahani S., Sekulic M., Cassol C., Blower L., Parwani A., Lal P., Feldman M.D., et al., An automated computational image analysis pipeline for histological grading of cardiac allograft rejection, Eur. Heart J. 42 (24) (2021) 2356–2369,.
[8]
Lipkova J., Chen T.Y., Lu M.Y., Chen R.J., Shady M., Williams M., Wang J., Noor Z., Mitchell R.N., Turan M., et al., Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies, Nat. Med. 28 (3) (2022) 575–582,.
[9]
Seraphin T.P., Luedde M., Roderburg C., van Treeck M., Scheider P., Buelow R.D., Boor P., Loosen S.H., Provaznik Z., Mendelsohn D., et al., Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning, Eur. Heart J.-Dig. Health 4 (3) (2023) 265–274,.
[10]
Graham S., Vu Q.D., Raza S.E.A., Azam A., Tsang Y.W., Kwak J.T., Rajpoot N., Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images, Med. Image Anal. 58 (2019),.
[11]
Ronneberger O., Fischer P., Brox T., U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015, pp. 234–241,.
[12]
Halinkovic M., Benesova W., SpringNet: A novel deep neural network architecture for histopathological image analysis, in: The International Conference on Innovations in Computing Research, Springer, 2022, pp. 65–75,.
[13]
Nirschl J.J., Janowczyk A., Peyster E.G., Frank R., Margulies K.B., Feldman M.D., Madabhushi A., Deep learning tissue segmentation in cardiac histopathology images, in: Deep Learning for Medical Image Analysis, Elsevier, 2017, pp. 179–195,.
[14]
Sirinukunwattana K., Pluim J.P., Chen H., Qi X., Heng P.-A., Guo Y.B., Wang L.Y., Matuszewski B.J., Bruni E., Sanchez U., et al., Gland segmentation in colon histology images: The glas challenge contest, Med. Image Anal. 35 (2017) 489–502,.
[15]
Chen H., Qi X., Cheng J., Heng P., Deep contextual networks for neuronal structure segmentation, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30, 2016,.
[16]
Nguyen L., Tosun A.B., Fine J.L., Lee A.V., Taylor D.L., Chennubhotla S.C., Spatial statistics for segmenting histological structures in H&E stained tissue images, IEEE Trans. Med. Imaging 36 (7) (2017) 1522–1532,.
[17]
Jayapandian C.P., Chen Y., Janowczyk A.R., Palmer M.B., Cassol C.A., Sekulic M., Hodgin J.B., Zee J., Hewitt S.M., O’Toole J., et al., Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains, Kidney Int. 99 (1) (2021) 86–101,.
[18]
Isensee F., Jaeger P.F., Kohl S.A., Petersen J., Maier-Hein K.H., nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation, Nature Methods 18 (2) (2021) 203–211.
[19]
Pati P., Jaume G., Fernandes L.A., Foncubierta-Rodríguez A., Feroce F., Anniciello A.M., Scognamiglio G., Brancati N., Riccio D., Di Bonito M., De Pietro G., Botti G., Goksel O., Thiran J.-P., Frucci M., Gabrani M., HACT-net: A hierarchical cell-to-tissue graph neural network for histopathological image classification, in: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, Springer International Publishing, Cham, 2020, pp. 208–219,.
[20]
Zhou Y., Graham S., Alemi Koohbanani N., Shaban M., Heng P.-A., Rajpoot N., Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images, in: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019,.
[21]
Chen R.J., Lu M.Y., Wang J., Williamson D.F., Rodig S.J., Lindeman N.I., Mahmood F., Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis, IEEE Trans. Med. Imaging (2020),.
[22]
Zhou Y., Onder O.F., Dou Q., Tsougenis E., Chen H., Heng P.-A., Cia-net: Robust nuclei instance segmentation with contour-aware information aggregation, in: Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings 26, Springer, 2019, pp. 682–693,.
[23]
Haruhara K., Tsuboi N., Sasaki T., Amano H., Tanaka M., Koike K., Kanzaki G., Okabayashi Y., Miyazaki Y., Ogura M., et al., Volume ratio of glomerular tufts to bowman capsules and renal outcomes in nephrosclerosis, Am. J. Hypertens. 32 (1) (2019) 45–53,.
[24]
Srivastava A., Palsson R., Kaze A.D., Chen M.E., Palacios P., Sabbisetti V., Betensky R.A., Steinman T.I., Thadhani R.I., McMahon G.M., et al., The prognostic value of histopathologic lesions in native kidney biopsy specimens: results from the Boston Kidney Biopsy Cohort Study, J. Am. Soc. Nephrol. 29 (8) (2018) 2213,.
[25]
Shao Y., Zhou K., Zhang L., CSSNet: Cascaded spatial shift network for multi-organ segmentation, Comput. Biol. Med. 170 (2024).
[26]
Chen Z.-M., Liao Y., Zhou X., Yu W., Zhang G., Ge Y., Ke T., Shi K., Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies, Comput. Biol. Med. 169 (2024).
[27]
Oktay O., Schlemper J., Folgoc L.L., Lee M., Heinrich M., Misawa K., Mori K., McDonagh S., Hammerla N.Y., Kainz B., et al., Attention u-net: Learning where to look for the pancreas, 2018,. arXiv preprint arXiv:1804.03999.
[28]
Aatresh A.A., Yatgiri R.P., Chanchal A.K., Kumar A., Ravi A., Das D., Raghavendra B., Lal S., Kini J., Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images, Comput. Med. Imaging Graph. 93 (2021),.
[29]
Lu M.Y., Williamson D.F., Chen T.Y., Chen R.J., Barbieri M., Mahmood F., Data-efficient and weakly supervised computational pathology on whole-slide images, Nat. Biomed. Eng. 5 (6) (2021) 555–570.
[30]
Wang X., Du Y., Yang S., Zhang J., Wang M., Zhang J., Yang W., Huang J., Han X., RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval, Med. Image Anal. 83 (2023),.
[31]
Billingham M., Cary N., Hammond M., Kemnitz J., Marboe C., McCallister H.A., Snovar D.C., Winters G., Zerbe A., A working formulation for the standardization of nomenclature in the diagnosis of heart and lung rejection: Heart Rejection Study Group. The International Society for Heart Transplantation, J Heart Transplant 9 (6) (1990) 587–593.
[32]
Stewart S., Winters G.L., Fishbein M.C., Tazelaar H.D., Kobashigawa J., Abrams J., Andersen C.B., Angelini A., Berry G.J., Burke M.M., et al., Revision of the 1990 working formulation for the standardization of nomenclature in the diagnosis of heart rejection, J. Heart Lung Transplant 24 (11) (2005) 1710–1720.
[33]
Bug D., Feuerhake F., Merhof D., Foreground extraction for histopathological whole slide imaging, in: Bildverarbeitung FÜR die medizin 2015, Springer, 2015, pp. 419–424,.
[34]
Chen L., Zhang H., Xiao J., Nie L., Shao J., Liu W., Chua T.-S., Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 5659–5667,.
[35]
Woo S., Park J., Lee J.-Y., Kweon I.S., Cbam: Convolutional block attention module, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 3–19,.
[36]
Dice L.R., Measures of the amount of ecologic association between species, Ecology 26 (3) (1945) 297–302,.
[37]
Kveton M., Hudec L., Vykopal I., Halinkovic M., Laco M., Felsoova A., Benesova W., Fabian O., Digital pathology in cardiac transplant diagnostics: From biopsies to algorithms, Cardiovasc. Pathol. (2023).
[38]
Angelini A., Andersen C.B., Bartoloni G., Black F., Bishop P., Doran H., Fedrigo M., Fries J.W., Goddard M., Goebel H., et al., A web-based pilot study of inter-pathologist reproducibility using the ISHLT 2004 working formulation for biopsy diagnosis of cardiac allograft rejection: the European experience, J. Heart Lung Transplant 30 (11) (2011) 1214–1220.

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Information & Contributors

Information

Published In

cover image Computers in Biology and Medicine
Computers in Biology and Medicine  Volume 177, Issue C
Jul 2024
720 pages

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 July 2024

Author Tags

  1. Computer vision
  2. Computer-aided diagnosis
  3. Deep learning
  4. Digital pathology

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