Volume 11 Issue 3 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 11 · NO. 3 | May 2024

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

Image Processing (1)

Computer-Aided Diagnosis (1)

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

Digital Pathology (2)

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Image Processing

Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images

Hanya Ahmed, Qianni Zhang, Robert Donnan, Akram Alomainy

Journal of Medical Imaging, Vol. 11, Issue 03, 034008, (April 2024) https://doi.org/10.1117/1.JMI.11.3.034008

TOPICS: Denoising, Optical coherence tomography, Transformers, Speckle, Dentistry, Silver, Visualization, Education and training, Image segmentation, Data modeling

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Computer-Aided Diagnosis

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal

Lindsay Douglas, Jordan Fuhrman, Qiyuan Hu, Alexandra Edwards, Deepa Sheth, Hiroyuki Abe, Maryellen Giger

Journal of Medical Imaging, Vol. 11, Issue 03, 034501, (May 2024) https://doi.org/10.1117/1.JMI.11.3.034501

TOPICS: Breast, Image segmentation, Education and training, Visualization, Magnetic resonance imaging, Tumors, Statistical analysis, Binary data, Image classification, Breast cancer

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Purpose

Current clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels.

Approach

A U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy c-means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings.

Results

Statistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, p<0.001). Scores from all breast regions performed significantly better than guessing (p<0.025 from the z-test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points.

Conclusions

Results demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

Image Perception, Observer Performance, and Technology Assessment

Perceptual thresholds for differences in CT noise texture

Luuk Oostveen, Kirsten Boedeker, Daniel Shin, Craig Abbey, Ioannis Sechopoulos

Journal of Medical Imaging, Vol. 11, Issue 03, 035501, (May 2024) https://doi.org/10.1117/1.JMI.11.3.035501

TOPICS: Computed tomography, Lung, Affine motion model, Reconstruction algorithms, Medical image reconstruction, Bone, Abdomen, Manufacturing, Covariance matrices, Brain

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Digital Pathology

Multiresolution semantic segmentation of biological structures in digital histopathology

Sina Salsabili, Adrian D. Chan, Eranga Ukwatta

Journal of Medical Imaging, Vol. 11, Issue 03, 037501, (May 2024) https://doi.org/10.1117/1.JMI.11.3.037501

TOPICS: Image segmentation, Semantics, Histopathology, Education and training, Lung, Blood vessels, Spatial resolution, Image resolution, Tissues, Performance modeling

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WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy

Paul Tourniaire, Marius Ilie, Julien Mazières, Anna Vigier, François Ghiringhelli, Nicolas Piton, Jean-Christophe Sabourin, Frédéric Bibeau, Paul Hofman, Nicholas Ayache, Hervé Delingette

Journal of Medical Imaging, Vol. 11, Issue 03, 037502, (May 2024) https://doi.org/10.1117/1.JMI.11.3.037502

TOPICS: Tissues, Tumors, Education and training, Matrices, Lung cancer, Lawrencium, Feature extraction, Biological research, Cross validation, Pathology

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Purpose

Immune checkpoint inhibitors (ICIs) are now one of the standards of care for patients with lung cancer and have greatly improved both progression-free and overall survival, although <20% of the patients respond to the treatment, and some face acute adverse events. Although a few predictive biomarkers have integrated the clinical workflow, they require additional modalities on top of whole-slide images and lack efficiency or robustness. In this work, we propose a biomarker of immunotherapy outcome derived solely from the analysis of histology slides.

Approach

We develop a three-step framework, combining contrastive learning and nonparametric clustering to distinguish tissue patterns within the slides, before exploiting the adjacencies of previously defined regions to derive features and train a proportional hazards model for survival analysis. We test our approach on an in-house dataset of 193 patients from 5 medical centers and compare it with the gold standard tumor proportion score (TPS) biomarker.

Results

On a fivefold cross-validation (CV) of the entire dataset, the whole-slide image-based survival analysis for patients treated with immunotherapy (WhARIO) features are able to separate a low- and a high-risk group of patients with a hazard ratio (HR) of 2.29 (CI95=1.48 to 3.56), whereas the TPS 1% reference threshold only reaches a HR of 1.81 (CI95=1.21 to 2.69). Combining the two yields a higher HR of 2.60 (CI95=1.72 to 3.94). Additional experiments on the same dataset, where one out of five centers is excluded from the CV and used as a test set, confirm these trends.

Conclusions

Our uniquely designed WhARIO features are an efficient predictor of survival for lung cancer patients who received ICI treatment. We achieve similar performance to the current gold standard biomarker, without the need to access other imaging modalities, and show that both can be used together to reach even better results.

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