We present LEPY, a free and openly available Python-based pipeline for the automated extraction and analysis of morphological and colour traits, from mounted specimens of Lepidoptera (butterflies and moths). The pipeline uses an automatically detected scale bar for accurate morphological measurements, together with image segmentation that separates the specimen from the background, with users able to pre-select from a set of segmentation models. We designed LEPY to be user-friendly and reproducible, ensuring efficient and consistent analysis of large image datasets. The pipeline also supports the integration of ultraviolet (UV) photographs for improved colour analysis, an innovative feature rarely available in existing trait-analysis tools. LEPY computes morphological traits such as body length, forewing length, and specimen area. It also extracts colour traits including hue, saturation, intensity from the red, green, and blue (RGB) channels, as well as brightness, contrast, chromaticity, and luminance from both RBG and UV channels. The pipeline uses the data to calculate colour diversity with the Shannon index, exports results in a structured, machine-readable format, and it also generates visual summaries of each image pair. We tested LEPY on different moth groups spanning a wide range of body sizes and colouration patterns. As an ecological case study, we applied the pipeline to complete datasets of Sphingidae and Saturniidae collected along an elevational gradient in the Peruvian Andes. The resulting trait data revealed taxon-dependent morphological and colour responses to elevation, thereby demonstrating LEPY's utility for analysing large-scale trait datasets. LEPY provides a robust and fully automated approach for the analysis of morphological and colour traits in Lepidoptera, supporting ecological and evolutionary research. Its scalability and ability to generate standardised, high-resolution trait datasets make it a valuable tool for biodiversity monitoring, macroecological research, and the development of global trait databases. • LEPY pipeline for automated trait extraction from Lepidoptera images. • Trait analysis includes body size, wing length, specimen area, and colour metrics. • Ultraviolet imaging adds novel insight to multispectral colour analysis. • Example dataset on moths sampled along an elevational gradient in Peru. • Large-scale, standardised trait data available for ecological research studies.

DPFR: Semi-supervised gland segmentation via density perturbation and feature recalibration.

The imaging technique called optical coherence tomography angiography (OCTA) has been used extensively in ophthalmology to identify eye conditions such as age-related macular degeneration, vascular occlusion or diabetic retinopathy. However, the multi-scale vascular architecture and noise from low image quality and eye diseases make it difficult to precisely segment the vasculature. In order to accurately segment the vasculature in OCTA, we introduced HV-OCTAMamba, a novel U-shaped network based on the Vision Mamba architecture. Inspired from the state-of-the-art models OCTAMamba and H-vmunet, HV-OCTAMamba integrates a Multi-Stream Efficient Embedding Module to extract local features, a Multi-Scale Dilated Asymmetric Convolution Module for multi-scale vasculature capturing, a Feature Recalibration and Filtering Module to filter noise and highlight target areas. The core component, the High-Order Visual State Space (H-VSS), improves feature consistency by modeling long-range dependencies through structured two-dimensional state-space (SS2D) operations. Our approach is appropriate for low-computation medical applications since it efficiently extracts the global and local features while preserving linear complexity. Extensive tests on the OCTA 3M, OCTA 6M, and ROSSA datasets showed that HV-OCTAMamba performs better than the most advanced methods in the state-of-the-art, offering a new benchmark for effective OCTA segmentation. Notably, HV-OCTAMamba achieved Dice coefficients of 87.45%, 83.18%, and 90.15% on the OCTA 3M, OCTA 6M, and ROSSA datasets, respectively. You may get the code at GitHub. 1 1 Code available at https://github.com/acvai/HV-OCTAMamba/ . • Proposing HV-OCTAMamba architecture, a novel lightweight U-shaped model for OCTA vessel segmentation. • Exploiting high-order visual state-space (H-VSS) for global context modeling. • Leveraging three new modules to enhance feature extraction and noise suppression. • Achieving consistent State-Of-The-Art performance on OCTA 3M, OCTA 6M, and ROSSA datasets. • Demonstrating computational efficiency suitable for real-world clinical settings.

SegMeshNet: Joint heart segmentation and mesh reconstruction with task-aware shared attention.

Improving nuclei segmentation in histopathological images: A hybrid deep learning architecture with self-attention and Multi-Scale Residual Dilated Blocks

Knowledge distillation for super-resolution reconstruction and segmentation in forward-facing camera images

MFD-UNet: minimum full-depth connected U-Net for accurate glandular segmentation in IHC images

Deep Learning-Driven Transmission Electron Microscopy Analysis of Murine Optic Nerve Myelinated Axons.

SAM2-driven dual-teacher framework using hierarchical cross-slice context for semi-supervised 3D medical image segmentation

Aggregate twice more efficiently: Dual feature aggregation transformer for medical image segmentation

FastSCVM: A lightweight hybrid Mamba–convolution network for efficient glaucoma image segmentation

KP2L: Knowledge-driven pyramid prototype learning for semi-supervised medical image segmentation

Dynamic ambiguity perception for semi-supervised medical image segmentation

DFEN: Dual feature equalization network for medical image segmentation

Efficient 3D medical image segmentation via Single-Slice Scribble Annotations: A near unsupervised approach

Core region induced probability intuitionistic fuzzy C-means clustering for infrared image segmentation

SPACE-Net: semantic-guided parallel aggregation and context-efficient network for satellite image segmentation

EGNet: A boundary-region closed-loop network for medical image segmentation with fuzzy lesions

Semi-supervised learning (SSL) has achieved notable progress in medical image segmentation. To achieve effective SSL, a model needs to be able to efficiently learn from limited labeled data and effectively exploit knowledge from abundant unlabeled data. Recent developments in visual foundation models, such as the Segment Anything Model (SAM), have demonstrated remarkable adaptability with improved sample efficiency. To seamlessly harness foundation models in SSL, we propose a SAM-driven cross prompting framework with adaptive sampling and prompt consistency for semi-supervised medical image segmentation, named CPAC-SAM. Our method employs SAM's unique prompt design and innovates a cross prompting strategy within a dual-branch framework to automatically generate prompts and supervision across two decoder branches, enabling effective learning from both scarce labeled and valuable unlabeled data. To ensure the quality of prompts for unlabeled data and provide meaningful supervision in the cross prompting scheme, we propose an innovative prototype-guided grid sampling strategy with adaptive intervals to simultaneously improve the reliability of the prompt selection area and ensure both adequate prompt density and complete target coverage. We further design a novel prompt consistency regularization to reduce SAM's prompt sensitivity and to enhance the output invariance under different prompts. We validate our method on five medical image segmentation tasks, encompassing both 2D and 3D scenarios. The extensive experiments with different labeled-data ratios and modalities demonstrate the superiority of our proposed method over the state-of-the-art SSL methods, with more than 4.1% and 3.8% Dice improvement on the breast cancer segmentation task and left atrium segmentation task, respectively. Our code is available at: https://github.com/JuzhengMiao/CPAC-SAM.

Window-xLSTMUnet: A medical image segmentation network fusing improved xLSTM with multi-scale receptive fields