Volume 12 Issue 1 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 12 · NO. 1 | January 2025

ISSUES IN PROGRESS

IN PROGRESS

SPIE publishes accepted journal articles as soon as they are approved for publication. Journal issues are considered In Progress until all articles for an issue have been published. Articles published ahead of the completed issue are fully citable.

IN THIS ISSUE

Computer-Aided Diagnosis (1)

Image Perception, Observer Performance, and Technology Assessment (1)

Biomedical Applications in Molecular, Structural, and Functional Imaging (1)

Digital Pathology (2)

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Computer-Aided Diagnosis

Predicting the risk of type 2 diabetes mellitus (T2DM) emergence in 5 years using mammography images: a comparison study between radiomics and deep learning algorithm

Nishta Letchumanan, Shouhei Hanaoka, Tomomi Takenaga, Yusuke Suzuki, Takahiro Nakao, Yukihiro Nomura, Takeharu Yoshikawa, Osamu Abe

Journal of Medical Imaging, Vol. 12, Issue 01, 014501, (January 2025) https://doi.org/10.1117/1.JMI.12.1.014501

TOPICS: Radiomics, Deep learning, Mammography, Cooccurrence matrices, Performance modeling, Feature extraction, Data modeling, Machine learning, Education and training, Diseases and disorders

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Image Perception, Observer Performance, and Technology Assessment

Examining the influence of digital phantom models in virtual imaging trials for tomographic breast imaging

Amar Kavuri, Mini Das

Journal of Medical Imaging, Vol. 12, Issue 01, 015501, (December 2024) https://doi.org/10.1117/1.JMI.12.1.015501

TOPICS: Breast, Digital breast tomosynthesis, Imaging systems, Breast density, Anatomy, Diagnostics, Tomography, Systems modeling, Spherical lenses, Quantum modeling

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Biomedical Applications in Molecular, Structural, and Functional Imaging

Backscattering Mueller matrix polarimetry estimates microscale anisotropy and orientation in complex brain tissue structure

Rhea Carlson, Courtney Comrie, Justina Bonaventura, Kellys Morara, Noelle Daigle, Elizabeth Hutchinson, Travis Sawyer

Journal of Medical Imaging, Vol. 12, Issue 01, 016001, (December 2024) https://doi.org/10.1117/1.JMI.12.1.016001

TOPICS: Tissues, Mueller matrices, Brain tissue, Backscatter, Polarimetry, Biological samples, Anisotropy, Polarization, Brain, Magnetic resonance imaging

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Digital Pathology

Dye amount quantification of Papanicolaou-stained cytological images by multispectral unmixing: spectral analysis of cytoplasmic mucin

Saori Takeyama, Tomoaki Watanabe, Nanxin Gong, Masahiro Yamaguchi, Takumi Urata, Fumikazu Kimura, Keiko Ishii

Journal of Medical Imaging, Vol. 12, Issue 01, 017501, (December 2024) https://doi.org/10.1117/1.JMI.12.1.017501

TOPICS: Dyes, Matrices, RGB color model, Cell biology, Statistical analysis, Absorbance, Multispectral imaging, Microscopes, Absorption, Image classification

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Weakly supervised pathological differentiation of primary central nervous system lymphoma and glioblastoma on multi-site whole slide images

Liping Wang, Lin Chen, Kaixi Wei, Huiyu Zhou, Reyer Zwiggelaar, Weiwei Fu, Yingchao Liu

Journal of Medical Imaging, Vol. 12, Issue 01, 017502, (January 2025) https://doi.org/10.1117/1.JMI.12.1.017502

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Purpose

Differentiating primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is crucial because their prognosis and treatment differ substantially. Manual examination of their histological characteristics is considered the golden standard in clinical diagnosis. However, this process is tedious and time-consuming and might lead to misdiagnosis caused by morphological similarity between their histology and tumor heterogeneity. Existing research focuses on radiological differentiation, which mostly uses multi-parametric magnetic resonance imaging. By contrast, we investigate the pathological differentiation between the two types of tumors using whole slide images (WSIs) of postoperative formalin-fixed paraffin-embedded samples.

Approach

To learn the specific and intrinsic histological feature representations from the WSI patches, a self-supervised feature extractor is trained. Then, the patch representations are fused by feeding into a weakly supervised multiple-instance learning model for the WSI classification. We validate our approach on 134 PCNSL and 526 GBM cases collected from three hospitals. We also investigate the effect of feature extraction on the final prediction by comparing the performance of applying the feature extractors trained on the PCNSL/GBM slides from specific institutions, multi-site PCNSL/GBM slides, and large-scale histopathological images.

Results

Different feature extractors perform comparably with the overall area under the receiver operating characteristic curve value exceeding 85% for each dataset and close to 95% for the combined multi-site dataset. Using the institution-specific feature extractors generally obtains the best overall prediction with both of the PCNSL and GBM classification accuracies reaching 80% for each dataset.

Conclusions

The excellent classification performance suggests that our approach can be used as an assistant tool to reduce the pathologists’ workload by providing an accurate and objective second diagnosis. Moreover, the discriminant regions indicated by the generated attention heatmap improve the model interpretability and provide additional diagnostic information.

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