Upscaling Prostate Cancer MRI Images to Cell-level Resolution Using Self-supervised Learning

Yaying Shi, Srijan Das, Yonghong Yan
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 254:226-236, 2024.

Abstract

Magnetic Resonance Imaging (MRI) plays a pivotal role in medical imaging, particularly in the diagnosis and treatment of cancers via radiography. However, one of the limitations of MRI is its low spatial resolution, which can hinder the accurate detection and characterization of cancerous lesions, especially those that are small or subtle in nature. There is a growing need for advancements in MRI technology to improve the resolution of MRI, particularly in the field of oncology, where precise detection and segmentation of tumors are crucial for effective treatment planning and optimal patient outcomes. In this paper, we proposed a self-supervised deep learning technique to upscale cancer MRI images to cell-level resolution with pathology Whole Slide Imaging (WSI). By integrating information from pathology WSIs with MRI images, this approach aims to create hybrid images that offer a more detailed and comprehensive view of cancer tissue structures. We evaluated our techniques using prostate lesions both on the similarity metrics and downstream segmentation tasks. For the similarity, our reconstructed fusion images can achieve an average 0.933 in structural similarity index. We improved lesion segmentation dice score from 57.3% to 64.0% on the test cases. Such fusion of the two imaging modalities shows promise for improving the accuracy and reliability of cancer diagnosis, guiding treatment decisions, and ultimately improving patient outcomes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v254-shi24a, title = {Upscaling Prostate Cancer MRI Images to Cell-level Resolution Using Self-supervised Learning}, author = {Shi, Yaying and Das, Srijan and Yan, Yonghong}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {226--236}, year = {2024}, editor = {Ciompi, Francesco and Khalili, Nadieh and Studer, Linda and Poceviciute, Milda and Khan, Amjad and Veta, Mitko and Jiao, Yiping and Haj-Hosseini, Neda and Chen, Hao and Raza, Shan and Minhas, FayyazZlobec, Inti and Burlutskiy, Nikolay and Vilaplana, Veronica and Brattoli, Biagio and Muller, Henning and Atzori, Manfredo and Raza, Shan and Minhas, Fayyaz}, volume = {254}, series = {Proceedings of Machine Learning Research}, month = {06 Oct}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v254/main/assets/shi24a/shi24a.pdf}, url = {https://proceedings.mlr.press/v254/shi24a.html}, abstract = {Magnetic Resonance Imaging (MRI) plays a pivotal role in medical imaging, particularly in the diagnosis and treatment of cancers via radiography. However, one of the limitations of MRI is its low spatial resolution, which can hinder the accurate detection and characterization of cancerous lesions, especially those that are small or subtle in nature. There is a growing need for advancements in MRI technology to improve the resolution of MRI, particularly in the field of oncology, where precise detection and segmentation of tumors are crucial for effective treatment planning and optimal patient outcomes. In this paper, we proposed a self-supervised deep learning technique to upscale cancer MRI images to cell-level resolution with pathology Whole Slide Imaging (WSI). By integrating information from pathology WSIs with MRI images, this approach aims to create hybrid images that offer a more detailed and comprehensive view of cancer tissue structures. We evaluated our techniques using prostate lesions both on the similarity metrics and downstream segmentation tasks. For the similarity, our reconstructed fusion images can achieve an average 0.933 in structural similarity index. We improved lesion segmentation dice score from 57.3% to 64.0% on the test cases. Such fusion of the two imaging modalities shows promise for improving the accuracy and reliability of cancer diagnosis, guiding treatment decisions, and ultimately improving patient outcomes.} }
Endnote
%0 Conference Paper %T Upscaling Prostate Cancer MRI Images to Cell-level Resolution Using Self-supervised Learning %A Yaying Shi %A Srijan Das %A Yonghong Yan %B Proceedings of the MICCAI Workshop on Computational Pathology %C Proceedings of Machine Learning Research %D 2024 %E Francesco Ciompi %E Nadieh Khalili %E Linda Studer %E Milda Poceviciute %E Amjad Khan %E Mitko Veta %E Yiping Jiao %E Neda Haj-Hosseini %E Hao Chen %E Shan Raza %E Fayyaz MinhasInti Zlobec %E Nikolay Burlutskiy %E Veronica Vilaplana %E Biagio Brattoli %E Henning Muller %E Manfredo Atzori %E Shan Raza %E Fayyaz Minhas %F pmlr-v254-shi24a %I PMLR %P 226--236 %U https://proceedings.mlr.press/v254/shi24a.html %V 254 %X Magnetic Resonance Imaging (MRI) plays a pivotal role in medical imaging, particularly in the diagnosis and treatment of cancers via radiography. However, one of the limitations of MRI is its low spatial resolution, which can hinder the accurate detection and characterization of cancerous lesions, especially those that are small or subtle in nature. There is a growing need for advancements in MRI technology to improve the resolution of MRI, particularly in the field of oncology, where precise detection and segmentation of tumors are crucial for effective treatment planning and optimal patient outcomes. In this paper, we proposed a self-supervised deep learning technique to upscale cancer MRI images to cell-level resolution with pathology Whole Slide Imaging (WSI). By integrating information from pathology WSIs with MRI images, this approach aims to create hybrid images that offer a more detailed and comprehensive view of cancer tissue structures. We evaluated our techniques using prostate lesions both on the similarity metrics and downstream segmentation tasks. For the similarity, our reconstructed fusion images can achieve an average 0.933 in structural similarity index. We improved lesion segmentation dice score from 57.3% to 64.0% on the test cases. Such fusion of the two imaging modalities shows promise for improving the accuracy and reliability of cancer diagnosis, guiding treatment decisions, and ultimately improving patient outcomes.
APA
Shi, Y., Das, S. & Yan, Y.. (2024). Upscaling Prostate Cancer MRI Images to Cell-level Resolution Using Self-supervised Learning. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 254:226-236 Available from https://proceedings.mlr.press/v254/shi24a.html.

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