Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches

Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images

Classification of cancer cellularity within tissue samples is currently a manual process performed by pathologists. This process of correctly determining cancer cellularity can be time intensive. Deep Learning (DL) techniques in particular have become increasingly more popular for this purpose, due to the accuracy and performance they exhibit, which can be comparable to the pathologists. This work investigates the capabilities of two DL approaches to assess cancer cellularity in whole slide images (WSI) in the SPIE-AAPM-NCI BreastPathQ challenge dataset. The effects of training on augmented data via rotations, and combinations of multiple architectures into a single network were analyzed using a modified Kendall Tau-b prediction probability metric known as the average prediction probability PK. A deep, transfer learned, Convolutional Neural Network (CNN) InceptionV3 was used as a baseline, achieving an average PK value of 0.884, showing improvement from the average PK value of 0.83 achieved by pathologists. The network was then trained on additional training datasets which were rotated between 1 and 360 degrees, which saw a peak increase of PK up to 4.2%. An additional architecture consisting of the InceptionV3 network and VGG16, a shallow, transfer learned CNN, was combined in a parallel architecture. This parallel architecture achieved a baseline average PK value of 0.907, a statistically significantly improvement over either of the architectures' performances separately (p<0.0001 by unpaired t-test).Comment: 7 pages (includes a cover page), 5 figures, 1 table, work addresses the BreastPathQ challeng

Estimation of cellularity in tumours treated with Neoadjuvant therapy: A comparison of Machine Learning algorithms

This paper describes a method for residual tumour cellularity (TC) estimation in Neoadjuvant treatment (NAT) of advanced breast cancer. This is determined manually by visual inspection by a radiologist, then an automated computation will contribute to reduce time workload and increase precision and accuracy. TC is estimated as the ratio of tumour area by total image area estimated after the NAT. The method proposed computes TC by using machine learning techniques trained with information on morphological parameters of segmented nuclei in order to classify regions of the image as tumour or normal. The data is provided by the 2019 SPIE Breast challenge, which was proposed to develop automated TC computation algorithms. Three algorithms were implemented: Support Vector Machines, Nearest K-means and Adaptive Boosting (AdaBoost) decision trees. Performance based on accuracy is compared and evaluated and the best result was obtained with Support Vector Machines. Results obtained by the methods implemented were submitted during ongoing challenge with a maximum of 0.76 of prediction probability of success

Developing a machine learning model for tumor cell quantification in standard histology images of lung cancer

Summary Background Tumor purity estimation plays a crucial role in genomic profiling and is traditionally carried out manually by pathologists. This manual approach has several disadvantages, including potential inaccuracies due to human error, inconsistency in evaluation criteria among different pathologists, and the time-consuming nature of the process. These issues may be addressed by adopting a digital approach. In this thesis, we employ a machine learning (ML)-based, cell- based classifier to estimate tumor purity in lung cancer tissues. Materials and methods In this study, conducted as part of the subsequent clinical trial TNM-I, we incorporated 61 patients diagnosed with non-small cell lung cancer (NSCLC). Tumor purity was initially estimated manually by two pathologists. The digital estimation of tumor purity was executed using a ML-based classifier in QuPath. To determine the level of agreement and inter-rater reliability between the two pathologists, as well as between the manual and digital estimations, we computed Intraclass Correlation Coefficient (ICC) and Cohen’s Kappa using SPSS. Results The ICC coefficient when comparing the tumor purity estimations done by the two pathologists was 0.833, indicating good reliability. According to Cohen’s Kappa the inter- rater reliability between the pathologists was moderate with a value of 0.534. The ICC coefficient when comparing the manual and digital tumor purity estimation was 0.838, which indicates good reliability. When analyzing for Cohen’s Kappa we got a value of 0.563, indicating moderate inter-rater reliability between the tumor purity estimations done manually and digitally. All the results were statistically significant. Conclusion In summary, we have successfully developed a ML classifier that estimates tumor purity in lung cancer tissue. Our findings align with previous research and demonstrate strong correlation with traditional detection methods. These results underscore the importance of continuing research in enhancing ML-based strategies for tumor purity estimation

Domain Generalization in Computational Pathology: Survey and Guidelines

Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming from a multitude of sources such as disparate imaging devices and diverse tissue preparation methods) can cause \emph{domain shift} (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data distributions, prompting the need for innovative \emph{domain generalization} (DG) solutions. Recognizing the potential of DG methods to significantly influence diagnostic and prognostic models in cancer studies and clinical practice, we present this survey along with guidelines on achieving DG in CPath. We rigorously define various DS types, systematically review and categorize existing DG approaches and resources in CPath, and provide insights into their advantages, limitations, and applicability. We also conduct thorough benchmarking experiments with 28 cutting-edge DG algorithms to address a complex DG problem. Our findings suggest that careful experiment design and CPath-specific Stain Augmentation technique can be very effective. However, there is no one-size-fits-all solution for DG in CPath. Therefore, we establish clear guidelines for detecting and managing DS depending on different scenarios. While most of the concepts, guidelines, and recommendations are given for applications in CPath, we believe that they are applicable to most medical image analysis tasks as well.Comment: Extended Versio

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Learning Deep Neural Networks for Enhanced Prostate Histological Image Analysis

In recent years, deep convolutional neural networks (CNNs) have shown promise for improving prostate cancer diagnosis by enabling quantitative histopathology through digital pathology. However, there are a number of factors that limit the widespread adoption and clinical utility of deep learning for digital pathology. One of these limitations is the requirement for large labelled training datasets which are expensive to construct due to limited availability of the requisite expertise. Additionally, digital pathology applications typically require the digitisation of histological slides at high magnifications. This process can be challenging especially when digitising large histological slides such as prostatectomies. This work studies and addresses these issues in two important applications of digital pathology: prostate nuclei detection and cell type classification. We study the performance of CNNs at different magnifications and demonstrate that it is possible to perform nuclei detection in low magnification prostate histopathology using CNNs with minimal loss in accuracy. We then study the training of prostate nuclei detectors in the small data setting and demonstrate that although it is possible to train nuclei detectors with minimal data, the models will be sensitive to hyperparameter choice and therefore may not generalise well. Instead, we show that pre-training the CNNs with colon histology data makes them more robust to hyperparameter choice. We then study the CNN performance for prostate cell type classification using supervised, transfer and semi-supervised learning in the small data setting. Our results show that transfer learning can be detrimental to performance but semi-supervised learning is able to provide significant improvements to the learning curve, allowing the training of neural networks with modest amounts of labelled data. We then propose a novel semi-supervised learning method called Deeply-supervised Exemplar CNNs and demonstrate their ability to improve the cell type classifier learning curves at a much better rate than previous semi-supervised neural network methods

J Orthop Res

Variations in chondrocyte density and organization in cartilage histology sections are associated with osteoarthritis progression. Rapid, accurate quantification of these two features can facilitate the evaluation of cartilage health and advance the understanding of their significance. The goal of this work was to adapt deep-learning-based methods to detect articular chondrocytes and chondrocyte clones from safranin-O-stained cartilage to evaluate chondrocyte cellularity and organization. The U-net and "you-only-look-once" (YOLO) models were trained and validated for identifying chondrocytes and chondrocyte clones, respectively. Validated models were then used to quantify chondrocyte and clone density in talar cartilage from Yucatan minipigs sacrificed 1 week, 3, 6, and 12 months after fixation of an intra-articular fracture of the hock joint. There was excellent/good agreement between expert researchers and the developed models in identifying chondrocytes/clones (U-net: R| \u2009=\u20090.93, y\u2009=\u20090.90x-0.69; median F1 score: 0.87/YOLO: R| \u2009=\u20090.79, y\u2009=\u20090.95x; median F1 score: 0.67). Average chondrocyte density increased 1 week after fracture (from 774 to 856 cells/mm| ), decreased substantially 3 months after fracture (610 cells/mm| ), and slowly increased 6 and 12 months after fracture (638 and 683 cells/mm| , respectively). Average detected clone density 3, 6, and 12 months after fracture (11, 11, 9 clones/mm| ) was higher than the 4-5 clones/mm| detected in normal tissue or 1 week after fracture and show local increases in clone density that varied across the joint surface with time. The accurate evaluation of cartilage cellularity and organization provided by this deep learning approach will increase objectivity of cartilage injury and regeneration assessments.W81XWH-11-1-0583/U.S. Department of Defense/W81XWH-10-1-0864/U.S. Department of Defense/P50 AR055533/AR/NIAMS NIH HHSUnited States/W81XWH-15-1-0642/U.S. Department of Defense/R49 CCR721745/CC/CDC HHSUnited States/P50 AR055533/AR/NIAMS NIH HHSUnited States/R49 CCR721745/CC/CDC HHSUnited States

High-resolution fluorescence endomicroscopy for rapid evaluation of breast cancer margins

Breast cancer is a major public health problem world-wide and the second leading cause of cancer-related female deaths. Breast conserving surgery (BCS), in the form of wide local excision (WLE), allows complete tumour resection while maintaining acceptable cosmesis. It is the recommended treatment for a large number of patients with early stage disease or, in more advanced cases, following neoadjuvant chemotherapy. About 30% of patients undergoing BCS require one or more re-operative interventions, mainly due to the presence of positive margins. The standard of care for surgical margin assessment is post-operative examination of histopathological tissue sections. However, this process is invasive, introduces sampling errors and does not provide real-time assessment of the tumour status of radial margins. The objective of this thesis is to improve intra-operative assessment of margin status by performing optical biopsy in breast tissue. This thesis presents several technical and clinical developments related to confocal fluorescence endomicroscopy systems for real-time characterisation of different breast morphologies. The imaging systems discussed employ flexible fibre-bundle based imaging probes coupled to high-speed line-scan confocal microscope set-up. A preliminary study on 43 unfixed breast specimens describes the development and testing of line-scan confocal laser endomicroscope (LS-CLE) to image and classify different breast pathologies. LS-CLE is also demonstrated to assess the intra-operative tumour status of whole WLE specimens and surgical excisions with high diagnostic accuracy. A third study demonstrates the development and testing of a bespoke LS-CLE system with methylene blue (MB), an US Food and Drug Administration (FDA) approved fluorescent agent, and integration with robotic scanner to enable large-area in vivo imaging of breast cancer. The work also addresses three technical issues which limit existing fibre-bundle based fluorescence endomicroscopy systems: i) Restriction to use single fluorescence agent due to low-speed, single excitation and single fluorescence spectral band imaging systems; ii) Limited Field of view (FOV) of fibre-bundle endomicroscopes due to small size of the fibre tip and iii) Limited spatial resolution of fibre-bundle endomicroscopes due to the spacing between the individual fibres leading to fibre-pixelation effects. Details of design and development of a high-speed dual-wavelength LS-CLE system suitable for high-resolution multiplexed imaging are presented. Dual-wavelength imaging is achieved by sequentially switching between 488 nm and 660 nm laser sources for alternate frames, avoiding spectral bleed-through, and providing an effective frame rate of 60 Hz. A combination of hand-held or robotic scanning with real-time video mosaicking, is demonstrated to enable large-area imaging while still maintaining microscopic resolution. Finally, a miniaturised piezoelectric transducer-based fibre-shifting endomicroscope is developed to enhance the resolution over conventional fibre-bundle based imaging systems. The fibre-shifting endomicroscope provides a two-fold improvement in resolution and coupled to a high-speed LS-CLE scanning system, provides real-time imaging of biological samples at 30 fps. These investigations furthered the utility and applications of the fibre-bundle based fluorescence systems for rapid imaging and diagnosis of cancer margins.Open Acces