ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing

Pathology image segmentation is an essential step in early detection and diagnosis for various diseases. Due to its complex nature, precise segmentation is not a trivial task. Recently, deep learning has been proved as an effective option for pathology image processing. However, its efficiency is highly restricted by inconsistent annotation quality. In this article, we propose an accurate and noise-tolerant segmentation approach to overcome the aforementioned issues. This approach consists of two main parts: a preprocessing module for data augmentation and a new neural network architecture, ANT-UNet. Experimental results demonstrate that, even on a noisy dataset, the proposed approach can achieve more accurate segmentation with 6% to 35% accuracy improvement versus other commonly used segmentation methods. In addition, the proposed architecture is hardware friendly, which can reduce the amount of parameters to one-tenth of the original and achieve 1.7× speed-up.

TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics

Computational Pathology (CPath) has seen rapid growth in recent years, driven by advanced deep learning (DL) algorithms. These algorithms typically share the same sequence of steps. However, due to the sheer size and complexity of handling large multi-gigapixel whole-slide images, there is no open-source software library that provides a generic end-to-end API for pathology image analysis using best practices for CPath. Most researchers have designed custom pipelines from the bottom-up, restricting the development of advanced CPath algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make CPath more accessible to new and advanced CPath scientists and pathologists alike. We provide a usable and adaptable library with efficient, cutting-edge and unit-tested tools for data loading, pre-processing, model inference, post-processing and visualization. This enables all kinds of users to easily build upon recent DL developments in the CPath literature. TIAToolbox provides a user-friendly modular API to enable seamless integration of advanced DL algorithms. We show with the help of examples how state-of-the-art DL algorithms can be streamlined using TIAToolbox.

VALIS: Virtual Alignment of pathoLogy Image Series

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

A deep learning-based iterative digital pathology annotation tool

ABSTRACTWell-annotated exemplars are an important prerequisite for supervised deep learning schemes. Unfortunately, generating these annotations is a cumbersome and laborious process, due to the large amount of time and effort needed. Here we present a deep-learning-based iterative digital pathology annotation tool that is both easy to use by pathologists and easy to integrate into machine vision systems. Our pathology image annotation tool greatly reduces annotation time from hours to a few minutes, while maintaining high fidelity with human-expert manual annotations. Here we demonstrate that our active learning tool can be used for a variety of pathology annotation tasks including masking tumor, stroma, and lymphocyte-rich regions, among others. This annotation automation system was validated on 90 unseen digital pathology images with tumor content from the CAMELYON16 database and it was found that pathologists’ gold standard masks were re-produced successfully using our tool. That is, an average of 2.7 positive selections (mouse clicks) and 8.0 negative selections (mouse clicks) were sufficient to generate tumor masks similar to pathologists’ gold standard in CAMELYON16 test WSIs. Furthermore, the developed image annotation tool has been used to build gold standard masks for hundreds of TCGA digital pathology images. This set was used to train a convolutional neural network for identification of tumor epithelium. The developed pan-cancer deep neural network was then tested on TCGA and internal data with comparable performance. The validated pathology image annotation tool described herein has the potential to be of great value in facilitating accurate, rapid pathological analysis of tumor biopsies.