Continual learning strategies for cancer-independent detection of lymph node metastases.

Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of expert pathologists. Many cancers, regardless of the site of origin, can metastasize to lymph nodes. However, collecting and annotating high-volume, high-quality datasets for every cancer type is challenging.

Human scattered tubular cells represent a heterogeneous population of glycolytic dedifferentiated proximal tubule cells.

Scattered tubular cells (STCs) are a phenotypically distinct cell population in the proximal tubule that increase in number after acute kidney injury. We aimed to characterize the human STC population. Three-dimensional human tissue analysis revealed that STCs are preferentially located within inner bends of the tubule and are barely present in young kidney tissue (<2 years), and their number increases with age.

Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection.

Poor generalizability is a major barrier to clinical implementation of artificial intelligence in digital pathology. The aim of this study was to test the generalizability of a pretrained deep learning model to a new diagnostic setting and to a small change in surgical indication. A deep learning model for breast cancer metastases detection in sentinel lymph nodes, trained on CAMELYON multicenter data, was used as a base model, and achieved an AUC of 0.

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies.

In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive.

Parietal epithelial cells maintain the epithelial cell continuum forming Bowman's space in focal segmental glomerulosclerosis.

In the glomerulus, Bowman's space is formed by a continuum of glomerular epithelial cells. In focal segmental glomerulosclerosis (FSGS), glomeruli show segmental scarring, a result of activated parietal epithelial cells (PECs) invading the glomerular tuft. The segmental scars interrupt the epithelial continuum.

Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks.

Modern pathology diagnostics is being driven toward large scale digitization of microscopic tissue sections. A prerequisite for its safe implementation is the guarantee that all tissue present on a glass slide can also be found back in the digital image. Whole-slide scanners perform a tissue segmentation in a low resolution overview image to prevent inefficient high-resolution scanning of empty background areas.

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.

Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization.

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline.

Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard.

Given the importance of gland morphology in grading prostate cancer (PCa), automatically differentiating between epithelium and other tissues is an important prerequisite for the development of automated methods for detecting PCa. We propose a new deep learning method to segment epithelial tissue in digitised hematoxylin and eosin (H&E) stained prostatectomy slides using immunohistochemistry (IHC) as reference standard. We used IHC to create a precise and objective ground truth compared to manual outlining on H&E slides, especially in areas with high-grade PCa.

1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset.

Background: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist.

New workflows and algorithms of bone scintigraphy based on SPECT-CT.

Gold standard bone scintigraphy workflow contains acquisition of planar anterior and posterior images and if necessary, additional SPECTs as well. Planar acquisitions are time consuming and not enough for accurately locating hotspots. Current paper proposes a novel workflow for fast whole body bone SPECT scintigraphy.

Automated material map generation from MRI scout pairs for preclinical PET attenuation correction.

A novel method is presented to perform material map segmentation from preclinical MRI for corresponding PET attenuation correction. MRI does not provide attenuation ratio, hence segmenting a material map from it is challenging. Furthermore the MRI images often suffer from ghost artifacts.

PET-CT based automated lung nodule detection.

An automatic method is presented in order to detect lung nodules in PET-CT studies. Using the foreground and background mean ratio independently in every nodule, we can detect the region of the nodules properly. The size and intensity of the lesions do not affect the result of the algorithm, although size constraints are present in the final classification step.

An extended registration framework for the triple registration of IBZM SPECT, DATSCAN SPECT and MRI brain images to support the evaluation of brain dopamine receptor scintigraphies.

An extended registration model is presented to register medical image triples acquired for brain dopamine receptor scintigraphies. The model operates with rigid and nonlinear transformations in parallel, where all transformation parameters are optimized by one optimization method. The concept of the transformation-sampling-similarity measurement minimizes the memory usage of a real implementation.

Hextuple registration of interim and follow-up PET-CT images for the accurate tracking of patient recovery after therapy.

An extended registration framework is presented to accurately register follow-up PET-CT study triples. Since there are six images to register, sophisticated feature extraction and similarity measurement methods are proposed. An irregular sampling method is introduced to decrease the processing speed of the hextuple registration.