MSNSegNet: attention-based multi-shape nuclei instance segmentation in histopathology images.

In clinical research, the segmentation of irregularly shaped nuclei, particularly in mesenchymal areas like fibroblasts, is crucial yet often neglected. These irregular nuclei are significant for assessing tissue repair in immunotherapy, a process involving neovascularization and fibroblast proliferation. Proper segmentation of these nuclei is vital for evaluating immunotherapy's efficacy, as it provides insights into pathological features. However, the challenge lies in the pronounced curvature variations of these non-convex nuclei, making their segmentation more difficult than that of regular nuclei. In this work, we introduce an undefined task to segment nuclei with both regular and irregular morphology, namely multi-shape nuclei segmentation. We propose a proposal-based method to perform multi-shape nuclei segmentation. By leveraging the two-stage structure of the proposal-based method, a powerful refinement module with high computational costs can be selectively deployed only in local regions, improving segmentation accuracy without compromising computational efficiency. We introduce a novel self-attention module to refine features in proposals for the sake of effectiveness and efficiency in the second stage. The self-attention module improves segmentation performance by capturing long-range dependencies to assist in distinguishing the foreground from the background. In this process, similar features get high attention weights while dissimilar ones get low attention weights. In the first stage, we introduce a residual attention module and a semantic-aware module to accurately predict candidate proposals. The two modules capture more interpretable features and introduce additional supervision through semantic-aware loss. In addition, we construct a dataset with a proportion of non-convex nuclei compared with existing nuclei datasets, namely the multi-shape nuclei (MsN) dataset. Our MSNSegNet method demonstrates notable improvements across various metrics compared to the second-highest-scoring methods. For all nuclei, the D i c e score improved by approximately 1.66 % , A J I by about 2.15 % , and D i c e obj by roughly 0.65 % . For non-convex nuclei, which are crucial in clinical applications, our method's A J I improved significantly by approximately 3.86 % and D i c e obj by around 2.54 % . These enhancements underscore the effectiveness of our approach on multi-shape nuclei segmentation, particularly in challenging scenarios involving irregularly shaped nuclei.

Weakly supervised histopathology image segmentation with self-attention.

Accurate segmentation in histopathology images at pixel-level plays a critical role in the digital pathology workflow. The development of weakly supervised methods for histopathology image segmentation liberates pathologists from time-consuming and labor-intensive works, opening up possibilities of further automated quantitative analysis of whole-slide histopathology images. As an effective subgroup of weakly supervised methods, multiple instance learning (MIL) has achieved great success in histopathology images. In this paper, we specially treat pixels as instances so that the histopathology image segmentation task is transformed into an instance prediction task in MIL. However, the lack of relations between instances in MIL limits the further improvement of segmentation performance. Therefore, we propose a novel weakly supervised method called SA-MIL for pixel-level segmentation in histopathology images. SA-MIL introduces a self-attention mechanism into the MIL framework, which captures global correlation among all instances. In addition, we use deep supervision to make the best use of information from limited annotations in the weakly supervised method. Our approach makes up for the shortcoming that instances are independent of each other in MIL by aggregating global contextual information. We demonstrate state-of-the-art results compared to other weakly supervised methods on two histopathology image datasets. It is evident that our approach has generalization ability for the high performance on both tissue and cell histopathology datasets. There is potential in our approach for various applications in medical images.

The effect of anode hydrodynamics on the sensitivity of microbial fuel cell based biosensors and the biological mechanism.

Fluid dynamics in the anodic chamber of a microbial fuel cell (MFC) is a key factor affecting the distribution of substrates and the efficiency of mass transport. However, the effect of hydrodynamics on MFC based biosensor (MFC-Biosensor) sensitivity has not been established. In this study, the three-dimension anode flow field of a two chamber MFC was visualized, and anodic configuration optimized by a reasonable serpentine flow field and inlet/outlet settings. Through optimization, the proportion of the dead zone in the anodic configuration decreased by 14.1%, and the velocity at the anode surface increased by 334.6% with better homogeneity of distribution. Moreover, electricity production and the sensitivity of MFC-Biosensors was improved by 42.0%, 46.1% and 52.3% for the detection of CTC, AVM and Hg, respectively. Biofilm viability analysis further proved that the enhanced surface velocity was of benefit for the permeation of toxicants into anodic biofilms, thus improving the sensor performance.

Effect of control mode on the sensitivity of a microbial fuel cell biosensor with Shewanella loihica PV-4 and the underlying bioelectrochemical mechanism.

A relatively poor sensitivity is a critical challenge for the application of microbial fuel cell biosensors (MFC-biosensors). This study investigated the effects of two control modes on sensor sensitivity and revealed the underlying bioelectrochemical mechanism. The results demonstrated that the sensitivity of an S. loihica PV-4 MFC-biosensor increased by 6.1 times when the anode was controlled at a constant potential (CP) instead of being operated with a fixed external resistance (ER). This obvious difference in sensor sensitivity was partly attributed to the masking effect of the observable offset current under ER mode and the lower electricity production capacity under CP mode. Moreover, the analysis of metabolic structure showed that under CP mode the anodic biofilm presented lower viability after toxic shock, due to a poorer ability to synthesize and secrete extracellular polymeric substances. Electrochemical measurements further revealed a lower capacitance under CP mode, which favored the permeation of Cd2+ into the biofilm.