Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy

Conventional methods for intraoperative histopathologic diagnosis are labour- and time-intensive, and may delay decision-making during brain-tumour surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain-tumour infiltration in fresh, unprocessed human tissues. Here, we demonstrate the first application of SRS microscopy in the operating room by using a portable fibre-laser-based microscope and unprocessed specimens from 101 neurosurgical patients. We also introduce an image-processing method – stimulated Raman histology (SRH) – which leverages SRS images to create virtual haematoxylin-and-eosin-stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, we found a remarkable concordance of SRH and conventional histology for predicting diagnosis (Cohen's kappa, κ > 0.89), with accuracy exceeding 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain-tumour subtype with 90% accuracy. Our findings provide insight into how SRH can now be used to improve the surgical care of brain tumour patients.

Digital pathology in the diagnostic setting: beyond technology into best practice and service management

Digital pathology (DP) and whole-slide imaging (WSI) technology have matured substantially over the last few years and there is growing evidence from validation studies that WSI is comparable to glass slides for histopathology diagnosis, although with some limitations, which can be appropriately minimised. Whether the controlled environment of validation studies translates to the same level of robustness when WSI is used in the actual diagnostic setting depends on the technical quality of WSI acquisition and on factors that influence the pre-image acquisition variables including the quality of glass slide inputs, and postimage acquisition variables such as access and use of WSI. The concept of 'DP service management' is introduced to fulfil the holistic needs of a laboratory intending to use the DP solution incorporating WSI for diagnostic purposes. The DP service management team should be an integral part of the diagnostic laboratory as it plays a central role undertaking responsibility to address an extensive range of issues from technical and training to governance and accreditation, hence ensuring a viable and sustainable diagnostic DP integration and usage. The pathologist as a specialist in the field and key decision maker of histopathology diagnoses has the duty and responsibility to acquaint and familiarise with DP and WSI when using the technology, especially on their indications and limitations, so as to take full advantage of these tools to enhance diagnostic quality.

Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors

BACKGROUND

Bioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference.

RESULTS

We evaluate our method on eight different bioimage classification tasks using the publicly available IICBU 2008 database. Each task comprises a separate dataset, and the collection represents typical subcellular, cellular, and tissue level classification problems. Our method demonstrates improved classification accuracy (0.9 to 9%) on six tasks when compared to state-of-the-art approaches. We also find that SDT outperforms the well-known dimension reduction techniques, with for example 0.2 to 13% improvement over linear discriminant analysis.

CONCLUSIONS

We present a general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm. Our evaluation on the IICBU 2008 database demonstrates improved performance over the state-of-the-art for six different classification tasks.

Multi-class texture analysis in colorectal cancer histology

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.