Volume 29 Issue 9 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 29 · NO. 9 | September 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

Special Section on Hyperspectral Imaging, Part 1 (4)

< Previous Issue | Next Issue >

Receive Email Alerts

VIEW ALL ABSTRACTS +

Special Section on Hyperspectral Imaging, Part 1

Effects of phantom microstructure on their optical properties

Jošt Stergar, Rok Hren, Matija Milanič

Journal of Biomedical Optics, Vol. 29, Issue 09, 093502, (May 2024) https://doi.org/10.1117/1.JBO.29.9.093502

TOPICS: Optical spheres, Scattering, Optical properties, Mie scattering, Absorption, Tissues, Microspheres, Reflectivity, Glasses, Transmittance

Read Abstract +

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery

Jeeseong Hwang, Philip Cheney, Stephen Kanick, Hanh N. Le, David McClatchy III, Helen Zhang, Nian Liu, Zhan-Qian John Lu, Tae Joon Cho, Kimberly Briggman, David Allen, Wendy Wells, Brian Pogue

Journal of Biomedical Optics, Vol. 29, Issue 09, 093503, (May 2024) https://doi.org/10.1117/1.JBO.29.9.093503

TOPICS: Tissues, Biological samples, Image segmentation, Tumors, Breast, Microscopy, Printed circuit board testing, Blood, Library classification systems, Surgery

Read Abstract +

Significance

Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries.

Aim

We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples.

Approach

Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes.

Results

The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas’ unique endmembers produced by the two methods agree with each other within <2% residual error margin.

Conclusions

Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas’ unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

Hyperspectral imaging of 4T1 mammary carcinomas grown in dorsal skinfold window chambers: spectral renormalization and fluorescence modeling

Tadej Tomanic, Tim Bozic, Bostjan Markelc, Jost Stergar, Gregor Sersa, Matija Milanic

Journal of Biomedical Optics, Vol. 29, Issue 09, 093504, (July 2024) https://doi.org/10.1117/1.JBO.29.9.093504

TOPICS: Tumors, Tissues, Hyperspectral imaging, Tumor growth modeling, Glasses, Skin, Reflectivity, Fluorescence, Image segmentation, Imaging systems

Read Abstract +

Significance

Hyperspectral imaging (HSI) of murine tumor models grown in dorsal skinfold window chambers (DSWCs) offers invaluable insight into the tumor microenvironment. However, light loss in a glass coverslip is often overlooked, and particular tissue characteristics are improperly modeled, leading to errors in tissue properties extracted from hyperspectral images.

Aim

We highlight the significance of spectral renormalization in HSI of DSWC models and demonstrate the benefit of incorporating enhanced green fluorescent protein (EGFP) excitation and emission in the skin tissue model for tumors expressing genes to produce EGFP.

Approach

We employed an HSI system for intravital imaging of mice with 4T1 mammary carcinoma in a DSWC over 14 days. We performed spectral renormalization of hyperspectral images based on the measured reflectance spectra of glass coverslips and utilized an inverse adding–doubling (IAD) algorithm with a two-layer murine skin model, to extract tissue parameters, such as total hemoglobin concentration and tissue oxygenation (StO2). The model was upgraded to consider EGFP fluorescence excitation and emission. Moreover, we conducted additional experiments involving tissue phantoms, human forearm skin imaging, and numerical simulations.

Results

Hyperspectral image renormalization and the addition of EGFP fluorescence in the murine skin model reduced the mean absolute percentage errors (MAPEs) of fitted and measured spectra by up to 10% in tissue phantoms, 0.55% to 1.5% in the human forearm experiment and numerical simulations, and up to 0.7% in 4T1 tumors. Similarly, the MAPEs for tissue parameters extracted by IAD were reduced by up to 3% in human forearms and numerical simulations. For some parameters, statistically significant differences (p<0.05) were observed in 4T1 tumors. Ultimately, we have shown that fluorescence emission could be helpful for 4T1 tumor segmentation.

Conclusions

The results contribute to improving intravital monitoring of DWSC models using HSI and pave the way for more accurate and precise quantitative imaging.

Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks

Minh Ha Tran, Ling Ma, Hasan Mubarak, Ofelia Gomez, James Yu, Michelle Bryarly, Baowei Fei

Journal of Biomedical Optics, Vol. 29, Issue 09, 093505, (July 2024) https://doi.org/10.1117/1.JBO.29.9.093505

TOPICS: Thyroid, Education and training, RGB color model, Hyperspectral imaging, Cancer detection, Cancer, Transformers, Machine learning, Image classification, Visualization

Read Abstract +

Significance

Hyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology.

Aim

The study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods.

Approach

Using an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension 250×250×84 pixels. We modified the TimeSformer network architecture, which used alternating spectral attention and spatial attention layers. We implemented several data augmentation methods for HSI based on the RandAugment algorithm. We compared the performances of TimeSformer on HSI against the performances of pretrained ConvNext and pretrained vision transformers (ViT) networks on red, green, and blue (RGB) images. Finally, we applied attention unrolling techniques on the trained TimeSformer network to identify the biological features to which the network paid attention.

Results

In the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted F1 score of 89.79%, a sensitivity of 91.50%, and an area under the receiving operator characteristic curve (AU-ROC) score of 97.04%. Additionally, TimeSformer produced thyroid carcinoma tumor margins with an average Jaccard score of 0.76 mm. Without data augmentation, TimeSformer achieved an accuracy of 88.23%, a weighted F1 score of 86.46%, a sensitivity of 85.53%, and an AU-ROC score of 94.94%. In comparison, the ViT network achieved an 89.98% accuracy, an 88.14% weighted F1 score, an 84.77% sensitivity, and a 96.17% AU-ROC. Our visualization results showed that the network paid attention to biological features.

Conclusions

The TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the F1 score, and the sensitivity score.

Significance

Aim

Approach

Results

Conclusions

Significance

Aim

Approach

Results

Conclusions

Significance

Aim

Approach

Results

Conclusions

Significance

Aim

Approach

Results

Conclusions

Keywords/Phrases

Search In:

Publication Years