Scholars@Duke
Jessica Hsing Wen Loo

Jessica Hsing Wen Loo

Affiliate
Biomedical Engineering

Selected Publications

Data from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

Other · January 8, 2025 <div>Abstract<p>Virtual staining for digital pathology has great potential to enable spatial biology research, improve efficiency and reliability in the clinical workflow, as well as conserve tissue samples in a nondestructive manner. I ... Full text Cite

Figure 5 from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

Other · January 8, 2025 <p>Examples of real (left) and virtual (right) stains showing key immune phenotypes. <b>A,</b> PD-L1–positive tumor. <b>B,</b> PD-L1–negative tumor. <b>C,</b> Immune-inflamed tumor with T-cell infiltration. ... Full text Cite

Figure 3 from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

Other · January 8, 2025 <p><b>A,</b> Example of real (left) and virtual (right) stains for a H&E WSI. <b>B,</b> Magnification series of real (left) and virtual (right) stains for concentric regions from the WSI at 10× (top), 20× (middle), ... Full text Cite

Table 3 from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

Other · January 8, 2025 <p>Pearson’s correlation (95% confidence interval) between measurements on real and virtual stains obtained from the cell segmentation–based analysis in Visiopharm software for PanCK, DAPI, PD-L1, CD3, and CD8 on testing slides. Analysis was ... Full text Cite

Figure 4 from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

Other · January 8, 2025 <p><b>A,</b> Example of real (left) and virtual (right) stains showing the WSI composite for all mIF stains. <b>B–E,</b> Examples of real (left) and virtual (right) stains from the individual models for DAPI + PanCK, P ... Full text Cite

Table 2 from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

Other · January 8, 2025 <p>Average Dice score (mean ± SD, median) between segmentations on real and virtual stains of six tumor subtypes on LUAD testing slides</p> ... Full text Cite

Table 4 from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

Other · January 8, 2025 <p>Pearson’s correlation (95% confidence interval) between measurements on real and virtual stains obtained from the colocalization analysis in Visiopharm software for CD3 and CD8 and CD3 and PD-L1 on testing slides. Analysis was performed ac ... Full text Cite

Table 1 from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

Other · January 8, 2025 <p>Average Dice score (mean ± SD, median) between segmentations on real and virtual stains of combined tumor, leukocyte aggregates, necrosis, and “other” categories on testing slides</p> ... Full text Cite

Figure 2 from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer

Other · January 8, 2025 <p>pIHC and segmentation algorithms were developed for mIF model development and evaluation. <b>A,</b> Examples of mIF (top) and the corresponding pIHC (bottom) for PanCK, PD-L1, CD3, and CD8. As per conventional practice, the res ... Full text Cite

Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer.

Journal Article Cancer research communications · December 2024 Virtual staining for digital pathology has great potential to enable spatial biology research, improve efficiency and reliability in the clinical workflow, as well as conserve tissue samples in a non-destructive manner. In this study, we demonstrate the fe ... Full text Cite

Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers.

Journal Article Ophthalmology science · September 2023 PurposeTo develop a fully-automatic hybrid algorithm to jointly segment and quantify biomarkers of polypoidal choroidal vasculopathy (PCV) on indocyanine green angiography (ICGA) and spectral domain-OCT (SD-OCT) images.DesignEvaluation of ... Full text Cite

Baseline Microperimetry and OCT in the RUSH2A Study: Structure-Function Association and Correlation With Disease Severity.

Journal Article Am J Ophthalmol · December 2022 PURPOSE: To investigate baseline mesopic microperimetry (MP) and spectral domain optical coherence tomography (OCT) in the Rate of Progression in USH2A-related Retinal Degeneration (RUSH2A) study. DESIGN: Natural history study METHODS: Setting: 16 clinical ... Full text Link to item Cite

Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2.

Journal Article The British journal of ophthalmology · March 2022 AimTo develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type 2 (MacTel2).MethodsThe dataset consisted of 99 eyes from 67 participants enrolled in an i ... Full text Cite

Open-Source Automatic Segmentation of Ocular Structures and Biomarkers of Microbial Keratitis on Slit-Lamp Photography Images Using Deep Learning.

Journal Article IEEE journal of biomedical and health informatics · January 2021 We propose a fully-automatic deep learning-based algorithm for segmentation of ocular structures and microbial keratitis (MK) biomarkers on slit-lamp photography (SLP) images. The dataset consisted of SLP images from 133 eyes with manual annotations by a p ... Full text Cite

Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.

Conference Ophthalmology · June 2020 PurposeTo validate the efficacy of a fully automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2).Design Full text Cite

Computational modeling of retinal hypoxia and photoreceptor degeneration in patients with age-related macular degeneration.

Journal Article PloS one · January 2019 Although drusen have long been acknowledged as a primary hallmark of dry age-related macular degeneration (AMD) their role in the disease remains unclear. We hypothesize that drusen accumulation increases the barrier to metabolite transport ultimately resu ... Full text Cite

Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2.

Journal Article Biomed Opt Express · June 1, 2018 Photoreceptor ellipsoid zone (EZ) defects visible on optical coherence tomography (OCT) are important imaging biomarkers for the onset and progression of macular diseases. As such, accurate quantification of EZ defects is paramount to monitor disease progr ... Full text Link to item Cite

Modeling the biomechanics of fetal movements.

Journal Article Biomechanics and modeling in mechanobiology · August 2016 Fetal movements in the uterus are a natural part of development and are known to play an important role in normal musculoskeletal development. However, very little is known about the biomechanical stimuli that arise during movements in utero, despite these ... Full text Cite