Minimum resolution requirements of digital pathology images for accurate classification

Digitization of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce

Content aware multi-focus image fusion for high-magnification blood film microscopy

Automated digital high-magnification optical microscopy is key to accelerating biology research and improving pathology clinical pathways. High magnification objectives with large numerical apertures are usually preferred to resolve the fine structural details of biological samples, but they have a very limited depth-of-field. Depending on the thickness of the sample, analysis of specimens typically requires the acquisition of multiple images at different focal planes for each field-of-view, followed by the fusion of these planes into an extended depth-of-field image. This translates into low scanning speeds, increased storage space, and processing time not suitable for high-throughput clinical use. We introduce a novel content-aware multi-focus image fusion approach based on deep learning which extends the depth-of-field of high magnification objectives effectively. We demonstrate the method with three examples, showing that highly accurate, detailed, extended depth of field images can be obtained at a lower axial sampling rate, using 2-fold fewer focal planes than normally required

Whole-Sample Mapping of Cancerous and Benign Tissue Properties

Structural and mechanical differences between cancerous and healthy tissue give rise to variations in macroscopic properties such as visual appearance and elastic modulus that show promise as signatures for early cancer detection. Atomic force microscopy (AFM) has been used to measure significant differences in stiffness between cancerous and healthy cells owing to its high force sensitivity and spatial resolution, however due to absorption and scattering of light, it is often challenging to accurately locate where AFM measurements have been made on a bulk tissue sample. In this paper we describe an image registration method that localizes AFM elastic stiffness measurements with high-resolution images of haematoxylin and eosin (H\&E)-stained tissue to within 1.5 microns. Color RGB images are segmented into three structure types (lumen, cells and stroma) by a neural network classifier trained on ground-truth pixel data obtained through k-means clustering in HSV color space. Using the localized stiffness maps and corresponding structural information, a whole-sample stiffness map is generated with a region matching and interpolation algorithm that associates similar structures with measured stiffness values. We present results showing significant differences in stiffness between healthy and cancerous liver tissue and discuss potential applications of this technique.Comment: Accepted at MICCAI201

Improving the feasibility of computer-assisted pathology through optimized tissue imaging

Diagnostic services are crucial to modern healthcare systems. Rapid processing of pathology results ensure patients enter the appropriate treatment pathway in a timely manner, and intraoperative pathology can maximize the effectiveness of interventions such as resection surgery. Despite a projected increase in demand, almost all pathology departments are severely understaffed; many pathologists are close to retirement and uptake of training places is low, adding further pressure. A proposed mitigation strategy is the digitization of pathology whereby samples are scanned and inspected as digital images, with the development of computer-aided diagnostic tools being the ultimate goal to reduce caseloads. This thesis focuses on two areas of computer-assisted pathology research: challenges preventing wider digitization of pathology, and the limited applicability of intraoperative pathology techniques. Two closely-related issues are high costs and pathologist perception of inadequate digital image quality. These issues have prevented routine use of digital slides, which has in turn hindered the development of automated tools through a lack of annotated data. In this thesis I quantify the minimum image quality necessary for accurate high-level diagnostics, showing that lower-resolution imaging is feasible to reduce costs while maintaining acceptable diagnostic accuracy. Intraoperative pathology currently relies on the time-consuming process of staining and assessing excised tissue while the patient remains under anaesthetic, limiting its applicability due to increased patient risk. I also describe a computer-assisted intraoperative diagnostic tool that combines nanomechanical measurements of tissue properties with a computer vision algorithm to infer the presence of cancerous tissue from an low-resolution image of the sample being assessed without the need for staining

AFM liver tissue data

This dataset consists of 27 tissue samples collected from 7 patients suffering from colorectal or pancreatic cancer metastasis who underwent curative liver resection surgery. Tissue punches were collected from healthy and tumour regions during surgery. Each section was measured using an atomic force microscope (AFM) to extract the elastic modulus of the tissue at multiple sites. Microscope images were captured of the whole sample as well as at each measurement site, allowing for spatial localization of each measurement. In some cases, the section was stained post-hoc with haemotoxylin and eosin (H&E), providing ground-truth validation of tissue pathology.There are two datasets available:1. The full liver tissue dataset (46.4 GB), which for each sample containsRaw and processed AFM measurement data for each measurement site (multiple formats: .csv and .mat for processed data)4X microscopy image of the unstained tissue sample (RGB TIFF)4X microscopy images of each measurement site (RGB TIFFs)Annotated image showing spatially localized measurement sites (RGB TIFF)MATLAB workspaces of the registration results (.mat)20X microscopy image of the post-measurement stained sample (RGB TIFF) (Note: not all samples contain stained images)Predicted whole-sample elastic modulus maps produced by a GAN detailed here (32-bit float TIFF)2. The image patch training dataset (8.84 MB):308 training and 78 validation sets, each containing:- 64 x 64-pixel unstained tissue sample image patches (8-bit grayscale)- 32 x 32-pixel tissue topology maps (32-bit float)- 32 x 32-pixel elastic modulus maps (32-bit float)</p