3D segmentation of biological structures is critical in biomedical imaging, offering significant insights into structures and functions. This paper introduces a novel segmentation of biological images that couples Multi-Aperture representation with Transformers for 3D (MAT3D) segmentation. Our method integrates the global context-awareness of Transformer networks with the local feature extraction capabilities of Convolutional Neural Networks (CNNs), providing a comprehensive solution for accurately delineating complex biological structures. First, we evaluated the performance of the proposed technique on two public clinical datasets of ACDC and Synapse multi-organ segmentation, rendering superior Dice scores of 93.34±0.05 and 89.73±0.04, respectively, with fewer parameters compared to the published literature. Next, we assessed the performance of our technique on an organoid dataset comprising four breast cancer subtypes. The proposed method achieved a Dice 95.12±0.02 and a PQ score of 97.01±0.01, respectively. MAT3D also significantly reduces the parameters to 40 million. The code is available on https://github.com/sohaibcs1/MAT3D.

MOTIVATION: Organization of the organoid models, imaged in 3D with a confocal microscope, is an essential morphometric index to assess responses to stress or therapeutic targets. In fact, differentiating malignant and normal cells is often difficult in monolayer cultures. But in 3D culture, colony organization can provide a clear set of indices for differentiating malignant and normal cells. The limiting factors are delineating each cell in a 3D colony in the presence of perceptual boundaries between adjacent cells and heterogeneity associated with cells being at different cell cycles. RESULTS: In a previous paper, we defined a potential field for delineating adjacent nuclei, with perceptual boundaries, in 2D histology images by coupling three deep networks. This concept is now extended to 3D and simplified by an enhanced cost function that replaces three deep networks with one. Validation includes four cell lines with diverse mutations, and a comparative analysis with the UNet models of microscopy indicates an improved performance with the F1-score of 0.83. AVAILABILITY AND IMPLEMENTATION: All software and annotated images are available through GitHub and Bioinformatics online. The software includes the proposed method, UNet for microscopy that was extended to 3D and report generation for profiling colony organization. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Nuclear delineation and phenotypic profiling are important steps in the automated analysis of histology sections. However, these are challenging problems due to (i) technical variations (e.g. fixation, staining) that originate as a result of sample preparation; (ii) biological heterogeneity (e.g. vesicular versus high chromatin phenotypes, nuclear atypia) and (iii) overlapping nuclei. This Application-Note couples contextual information about the cellular organization with the individual signature of nuclei to improve performance. As a result, routine delineation of nuclei in H&E stained histology sections is enabled for either computer-aided pathology or integration with genome-wide molecular data. RESULTS: The method has been evaluated on two independent datasets. One dataset originates from our lab and includes H&E stained sections of brain and breast samples. The second dataset is publicly available through IEEE with a focus on gland-based tissue architecture. We report an approximate AJI of 0.592 and an F1-score 0.93 on both datasets. AVAILABILITY AND IMPLEMENTATION: The code-base, modified dataset and results are publicly available. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Free-Space-Optical (FSO) communication has the potential to provide optical-level wireless communication speeds. It can also help solve the wireless capacity problem experienced by the traditional RF-based technologies. Despite its capacity advantages, FSO communication is prone to mobility. Since the FSO transceivers are highly directional, they require establishment and maintenance of line-of-sight (LOS) between each other. We consider two autonomous mobile nodes, each with one FSO transceiver mounted on a movable head capable of rotating 360 degree. We propose a novel scheme that deals with the problem of automatic maintenance of LOS alignment between the two nodes with mechanical steering of the FSO transceivers. We design protocols to maintain an FSO link between the mobiles satisfying a minimum received power or signal-to-noise ratio (SNR). We also present a prototype implementation of such mobile node with FSO transceivers. The effectiveness of the alignment protocol is evaluated by analyzing the results obtained from both simulations and also experiments conducted using the prototype. The results show that, by using such mechanically steerable transceivers and a simple auto-alignment mechanism, it is possible to maintain optical wireless links in a mobile setting with nominal disruption.