Significance: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. Aim: The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright-field microscopy images that contain white blood cells (WBCs). Approach: We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate in vitro cancer cells from WBCs. The second approach is based on faster region-based convolutional neural network (Faster R-CNN). Results: Both approaches detected cancer cells with higher than 95% sensitivity and 99% specificity with the Faster R-CNN being more efficient and suitable for deployment presenting an improvement of 4% in sensitivity. The distinctive feature that CNN uses for discrimination is cell size, however, in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. Conclusions: CNN-based DL approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.

A prior work describing a computer vision system for measuring maturity using longitudinal views of cotton fibers reported observing a large variation in maturity within a single fiber. This paper describes the use of confocal microscopy as an independent measuring method to validate those findings. Individual cotton fibers are imaged from end to end by a confocal microscope producing hundreds of image volumes of cotton fiber segments, each ∼150 µm in length. From these volumes, virtual fiber cross-sections are extracted, processed using the level set method, and measured according to AATCC standards. The results demonstrate with both visual and quantitative analysis that fiber maturity can exhibit large variations within a single fiber.

Currently, detection of circulating tumor cells (CTCs) in cancer patient blood samples relies on immunostaining, which does not provide access to live CTCs, limiting the breadth of CTC-based applications. As a first step to address this limitation, here, we demonstrate staining-free enumeration of tumor cells spiked into lysed blood samples using digital holographic microscopy (DHM), microfluidics and machine learning (ML). A 3D-printed module for laser assembly was developed to simplify the optical set up for holographic imaging of cells flowing through a sheath-based microfluidic device. Computational reconstruction of the holograms was performed to localize the cells in 3D and obtain the plane of best focus images to train deep learning models. First, we evaluated the classification performance of two convolutional neural networks (CNNs): ResNet-50 and a custom-designed shallow Network dubbed s-Net. The accuracy, sensitivity and specificity of these networks were found to range from 97.08% and 99.32%. Upon selecting the s-Net due to its simple architecture and low computational burden, we formulated a decision gating strategy to significantly lower the false positive rate (FPR). By applying an optimized decision threshold to mixed samples prepared in silico , the FPR was reduced from 1×10 ⁻² to 2.77×10 ⁻⁴ . Finally, the developed DHM-ML framework was successfully applied to enumerate spiked MCF-7 breast cancer cells from lysed blood samples containing a background of white blood cells (WBCs). We conclude by discussing the advances that need to be made to translate the DHM-ML approach to staining-free enumeration of CTCs in cancer patient blood samples.

Significance Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. Aim The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright field microscopy images that contain white blood cells (WBCs). Approach We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate cancer cells from WBCs. The second approach is based on Faster Region-based Convolutional Neural Network (Faster R-CNN). Results Both approaches detected cancer cells with high sensitivity and specificity with the Faster R-CNN being more efficient and suitable for deployment. The distinctive feature used by the CNN used to discriminate is cell size, however in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. Conclusions CNN-based deep learning approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.

Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as $2\mathrm{cm}\times 2\mathrm{cm}$ with an angular resolution limit of $\pm 3°$. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency.

We introduce a novel Maximum Entropy (MaxEnt) framework that can generate 3D scenes by incorporating objects’ relevancy, hierarchical and contextual constraints in a unified model. This model is formulated by a Gibbs distribution, under the MaxEnt framework, that can be sampled to generate plausible scenes. Unlike existing approaches, which represent a given scene by a single And-Or graph, the relevancy constraint (defined as the frequency with which a given object exists in the training data) require our approach to sample from multiple And-Or graphs, allowing variability in terms of objects’ existence across synthesized scenes. Once an And-Or graph is sampled from the ensemble, the hierarchical constraints are employed to sample the Or-nodes (style variations) and the contextual constraints are subsequently used to enforce the corresponding relations that must be satisfied by the And-nodes. To illustrate the proposed methodology, we use desk scenes that are composed of objects whose existence, styles and arrangements (position and orientation) can vary from one scene to the next. The relevancy, hierarchical and contextual constraints are extracted from a set of training scenes and utilized to generate plausible synthetic scenes that in turn satisfy these constraints. After applying the proposed framework, scenes that are plausible representations of the training examples are automatically generated.