Scattering Inversion Study for Suspended Label-Free Lymphocytes with Complex Fine StructuresRead the full article
The open access journal BMEF (BME Frontiers), published in association with SIBET CAS, is a platform for the multidisciplinary community of biomedical engineering, publishing wide-ranging research in the field.
BMEF's editorial board is led by Xingde Li (Johns Hopkins University), Yuguo Tang (Suzhou Institute of Biomedical Engineering and Technology), and Guoqi Zhang (Delft University of Technology) and is comprised of leading experts in the field of biomedical engineering.
Special Issues now open for submissions:
Latest ArticlesMore articles
Molecular Computational Anatomy: Unifying the Particle to Tissue Continuum via Measure Representations of the Brain
Objective. The objective of this research is to unify the molecular representations of spatial transcriptomics and cellular scale histology with the tissue scales of computational anatomy for brain mapping. Impact Statement. We present a unified representation theory for brain mapping based on geometric varifold measures of the microscale deterministic structure and function with the statistical ensembles of the spatially aggregated tissue scales. Introduction. Mapping across coordinate systems in computational anatomy allows us to understand structural and functional properties of the brain at the millimeter scale. New measurement technologies in digital pathology and spatial transcriptomics allow us to measure the brain molecule by molecule and cell by cell based on protein and transcriptomic functional identity. We currently have no mathematical representations for integrating consistently the tissue limits with the molecular particle descriptions. The formalism derived here demonstrates the methodology for transitioning consistently from the molecular scale of quantized particles—using mathematical structures as first introduced by Dirac as the class of generalized functions—to the tissue scales with methods originally introduced by Euler for fluids. Methods. We introduce two mathematical methods based on notions of generalized functions and statistical mechanics. We use geometric varifolds, a product measure on space and function, to represent functional states at the micro-scales—electrophysiology, molecular histology—integrated with a Boltzmann-like program to pass from deterministic particle descriptions to empirical probabilities on the functional states at the tissue scales. Results. Our space-function varifold representation provides a recipe for traversing from molecular to tissue scales in terms of a cascade of linear space scaling composed with nonlinear functional feature mapping. Following the cascade implies every scale is a geometric measure so that a universal family of measure norms can be introduced which quantifies the geodesic connection between brains in the orbit independent of the probing technology, whether it be RNA identities, Tau or amyloid histology, spike trains, or dense MR imagery. Conclusions. We demonstrate a unified brain mapping theory for molecular and tissue scales based on geometric measure representations. We call the consistent aggregation of tissue scales from particle and cellular scales, molecular computational anatomy.
Simulated MRI Artifacts: Testing Machine Learning Failure Modes
Objective. Seven types of MRI artifacts, including acquisition and preprocessing errors, were simulated to test a machine learning brain tumor segmentation model for potential failure modes. Introduction. Real-world medical deployments of machine learning algorithms are less common than the number of medical research papers using machine learning. Part of the gap between the performance of models in research and deployment comes from a lack of hard test cases in the data used to train a model. Methods. These failure modes were simulated for a pretrained brain tumor segmentation model that utilizes standard MRI and used to evaluate the performance of the model under duress. These simulated MRI artifacts consisted of motion, susceptibility induced signal loss, aliasing, field inhomogeneity, sequence mislabeling, sequence misalignment, and skull stripping failures. Results. The artifact with the largest effect was the simplest, sequence mislabeling, though motion, field inhomogeneity, and sequence misalignment also caused significant performance decreases. The model was most susceptible to artifacts affecting the FLAIR (fluid attenuation inversion recovery) sequence. Conclusion. Overall, these simulated artifacts could be used to test other brain MRI models, but this approach could be used across medical imaging applications.
Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning
The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies, and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.
Deep UV Microscopy Identifies Prostatic Basal Cells: An Important Biomarker for Prostate Cancer Diagnostics
Objective and Impact Statement. Identifying benign mimics of prostatic adenocarcinoma remains a significant diagnostic challenge. In this work, we developed an approach based on label-free, high-resolution molecular imaging with multispectral deep ultraviolet (UV) microscopy which identifies important prostate tissue components, including basal cells. This work has significant implications towards improving the pathologic assessment and diagnosis of prostate cancer. Introduction. One of the most important indicators of prostate cancer is the absence of basal cells in glands and ducts. However, identifying basal cells using hematoxylin and eosin (H&E) stains, which is the standard of care, can be difficult in a subset of cases. In such situations, pathologists often resort to immunohistochemical (IHC) stains for a definitive diagnosis. However, IHC is expensive and time-consuming and requires more tissue sections which may not be available. In addition, IHC is subject to false-negative or false-positive stains which can potentially lead to an incorrect diagnosis. Methods. We leverage the rich molecular information of label-free multispectral deep UV microscopy to uniquely identify basal cells, luminal cells, and inflammatory cells. The method applies an unsupervised geometrical representation of principal component analysis to separate the various components of prostate tissue leading to multiple image representations of the molecular information. Results. Our results show that this method accurately and efficiently identifies benign and malignant glands with high fidelity, free of any staining procedures, based on the presence or absence of basal cells. We further use the molecular information to directly generate a high-resolution virtual IHC stain that clearly identifies basal cells, even in cases where IHC stains fail. Conclusion. Our simple, low-cost, and label-free deep UV method has the potential to improve and facilitate prostate cancer diagnosis by enabling robust identification of basal cells and other important prostate tissue components.
Laser-Treated Surfaces for VADs: From Inert Titanium to Potential Biofunctional Materials
Objective. Laser-treated surfaces for ventricular assist devices. Impact Statement. This work has scientific impact since it proposes a biofunctional surface created with laser processing in bioinert titanium. Introduction. Cardiovascular diseases are the world’s leading cause of death. An especially debilitating heart disease is congestive heart failure. Among the possible therapies, heart transplantation and mechanical circulatory assistance are the main treatments for its severe form at a more advanced stage. The development of biomaterials for ventricular assist devices is still being carried out. Although polished titanium is currently employed in several devices, its performance could be improved by enhancing the bioactivity of its surface. Methods. Aiming to improve the titanium without using coatings that can be detached, this work presents the formation of laser-induced periodic surface structures with a topology suitable for cell adhesion and neointimal tissue formation. The surface was modified by femtosecond laser ablation and cell adhesion was evaluated in vitro by using fibroblast cells. Results. The results indicate the formation of the desired topology, since the cells showed the appropriate adhesion compared to the control group. Scanning electron microscopy showed several positive characteristics in the cells shape and their surface distribution. The in vitro results obtained with different topologies point that the proposed LIPSS would provide enhanced cell adhesion and proliferation. Conclusion. The laser processes studied can create new interactions in biomaterials already known and improve the performance of biomaterials for use in ventricular assist devices.
Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations
Objective and Impact Statement. We use deep learning models to classify cervix images—collected with a low-cost, portable Pocket colposcope—with biopsy-confirmed high-grade precancer and cancer. We boost classification performance on a screened-positive population by using a class-balanced loss and incorporating green-light colposcopy image pairs, which come at no additional cost to the provider. Introduction. Because the majority of the 300,000 annual deaths due to cervical cancer occur in countries with low- or middle-Human Development Indices, an automated classification algorithm could overcome limitations caused by the low prevalence of trained professionals and diagnostic variability in provider visual interpretations. Methods. Our dataset consists of cervical images () from 880 patient visits. After optimizing the network architecture and incorporating a weighted loss function, we explore two methods of incorporating green light image pairs into the network to boost the classification performance and sensitivity of our model on a test set. Results. We achieve an area under the receiver-operator characteristic curve, sensitivity, and specificity of 0.87, 75%, and 88%, respectively. The addition of the class-balanced loss and green light cervical contrast to a Resnet-18 backbone results in a 2.5 times improvement in sensitivity. Conclusion. Our methodology, which has already been tested on a prescreened population, can boost classification performance and, in the future, be coupled with Pap smear or HPV triaging, thereby broadening access to early detection of precursor lesions before they advance to cancer.