Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization

ULST: U-shaped LeWin Spectral Transformer for virtual staining of pathological sections

At present, pathological section staining faces several challenges, including complex sample preparation and stringent infrastructure requirements. Virtual staining methods utilizing deep neural networks to automatically generate stained images are gaining recognition. However, most current virtual staining techniques rely on standard RGB microscopy, which lacks spatial spectral information. In contrast, hyperspectral imaging of pathological sections provides rich spatial spectral data while maintaining high resolution. To address this issue, the U-shaped Locally-enhanced Window (LeWin) Spectral Transformer (ULST) was developed to convert unstained hyperspectral microscopic images into RGB equivalents of hematoxylin and eosin (HE) stained samples. The LeWin Spectral Transformer (LST) block within ULST takes full advantage of the transformer's attention extraction capabilities. It applies local self-attention in the spatial domain using non-overlapping windows to capture local context while significantly reducing computational complexity for high-resolution feature maps and preserving spatial features from hyperspectral images (HSI). Furthermore, the Spectral Transformer collects spectral features without losing spatial information. By integrating a multi-scale encoder-bottle-decoder structure in a U-shaped network configuration with sequential symmetric connections of LSTs, ULST performs virtual HE staining on microscopic images of unstained hyperspectral pathological sections. Qualitative and quantitative experiments show that ULST performs better than other advanced virtual staining methods in the virtual HE staining task.

State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues

Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures and tissues. Despite its importance, the segmentation process encounters significant challenges, including variability in imaging conditions, complex biological structures, and artefacts (e.g., noise), which can compromise the accuracy of traditional methods. The emergence of deep learning (DL) has catalyzed substantial advancements in addressing these issues. This systematic literature review (SLR) provides a comprehensive overview of state-of-the-art DL methods developed over the past six years for the segmentation of microscopic images. We critically analyze key contributions, emphasizing how these methods specifically tackle challenges in cell, nucleus, and tissue segmentation. Additionally, we evaluate the datasets and performance metrics employed in these studies. By synthesizing current advancements and identifying gaps in existing approaches, this review not only highlights the transformative potential of DL in enhancing diagnostic accuracy and research efficiency but also suggests directions for future research. The findings of this study have significant implications for improving methodologies in medical and biological applications, ultimately fostering better patient outcomes and advancing scientific understanding.

NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images

In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available on the respective repositories.

FUSeg: The Foot Ulcer Segmentation Challenge

Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment. Unfortunately, this process is very time-consuming and requires a high level of expertise, hence the need for automatic wound measurement methods. Recently, automatic wound segmentation methods based on deep learning have shown promising performance; yet, they heavily rely on large training datasets. A few wound image datasets were published including the Diabetic Foot Ulcer Challenge dataset, the Medetec wound dataset, and WoundDB. Existing public wound image datasets suffer from small size and a lack of annotation. There is a need to build a fully annotated dataset to benchmark wound segmentation methods. To address these issues, we propose the Foot Ulcer Segmentation Challenge (FUSeg), organized in conjunction with the 2021 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). It contains 1210 pixel-wise annotated foot ulcer images collected over 2 years from 889 patients. The submitted algorithms are reviewed in this paper and the dataset can be accessed through the Foot Ulcer Segmentation Challenge website.