BME frontiers最新文献

Objective and Impact Statement. Distinguishing malignant lymphocytes from normal ones is vital in pathological examination. We proposed an inverse light scattering (ILS) method for label-free suspended lymphocytes with complex fine structures to identify their volumes for pathological state. Introduction. Light scattering as cell's "fingerprint" provides valuable morphology information closely related to its biophysical states. However, the detail relationships between the morphology with complex fine structures and its scattering characters are not fully understood. Methods. To quantitatively inverse the volumes of membrane and nucleus as the main scatterers, clinical lymphocyte morphologies were modeled combining the Gaussian random sphere geometry algorithm by 750 reconstructed results after confocal scanning, which allowed the accurate simulation to solve ILS problem. For complex fine structures, the specificity for ILS study was firstly discussed (to our knowledge) considering the differences of not only surface roughness, posture, but also the ratio of nucleus to the cytoplasm and refractive index. Results. The volumes of membrane and nucleus were proved theoretically to have good linear relationship with the effective area and entropy of forward scattering images. Their specificity deviations were less than 3.5%. Then, our experimental results for microsphere and clinical leukocytes showed the Pearson product-moment correlation coefficients (PPMCC) of this linear relationship were up to 0.9830~0.9926. Conclusion. Our scattering inversion method could be effectively applied to identify suspended label-free lymphocytes without destructive sample pretreatments and complex experimental systems.

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.

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.

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.

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 ($n=\mathrm{1,760}$) 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.

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.

Objective. This paper is an initial work towards developing particle-mediated histotripsy (PMH) as a novel method of treating catheter-based medical device (CBMD) intraluminal biofilms. Impact Statement. CBMDs commonly become infected with bacterial biofilms leading to medical device failure, infection, and adverse patient outcomes. Introduction. Histotripsy is a noninvasive focused ultrasound ablation method that was recently proposed as a novel method to remove intraluminal biofilms. Here, we explore the potential of combining histotripsy with acoustically active particles to develop a PMH approach that can noninvasively remove biofilms without the need for high acoustic pressures or real-time image guidance for targeting. Methods. Histotripsy cavitation thresholds in catheters containing either gas-filled microbubbles (MBs) or fluid-filled nanocones (NCs) were determined. The ability of these particles to sustain cavitation over multiple ultrasound pulses was tested after a series of histotripsy exposures. Next, the ability of PMH to generate selective intraluminal cavitation without generating extraluminal cavitation was tested. Finally, the biofilm ablation and bactericidal capabilities of PMH were tested using both MBs and NCs. Results. PMH significantly reduced the histotripsy cavitation threshold, allowing for selective luminal cavitation for both MBs and NCs. Results further showed PMH successfully removed intraluminal biofilms in Tygon catheters. Finally, results from bactericidal experiments showed minimal reduction in bacteria viability. Conclusion. The results of this study demonstrate the potential for PMH to provide a new modality for removing bacterial biofilms from CBMDs and suggest that additional work is warranted to develop histotripsy and PMH for treatment of CBMD intraluminal biofilms.