LNDb challenge on automatic lung cancer patient management.

Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly and time-consuming. This has fuelled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules.

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions.

Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex.

Collection of unbiased stereology data currently relies on relatively simple, low throughput technology developed in the mid-1990s. In an effort to improve the accuracy and efficiency of these integrated hardware-software-digital microscopy systems, we have developed an automatic segmentation algorithm (ASA) for automatic stereology counts using the unbiased optical fractionator method. Here we report on a series of validation experiments in which immunostained neurons (NeuN) and microglia (Iba1) were automatically counted in tissue sections through a mouse neocortex.

Automatic stereology of mean nuclear size of neurons using an active contour framework.

The use of unbiased stereology to quantify structural parameters such as mean cell and nuclear size (area and volume) can be useful for a wide variety of biological studies. Here we propose a novel segmentation framework using an Active Contour Model to automate the collection of stereology from stained cells and other objects in tissue sections. This approach is demonstrated for stained brain sections from young adult Fischer 344 rats.

Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology.

In recent decades stereology-based studies have played a significant role in understanding brain aging and developing novel drug discovery strategies for treatment of neurological disease and mental illness. A major obstacle to further progress in a wide range of neuroscience sub-disciplines remains the lack of high-throughput technology for stereology analyses. Though founded on methodologically unbiased principles, commercially available stereology systems still rely on well-trained humans to manually count hundreds of cells within each region of interest (ROI).

A novel image segmentation method for the evaluation of inflammation-induced cortical and hippocampal white matter injury in neonatal mice.

The developing brain is very susceptible to environmental insults, and very immature infants often suffer from long-term neurological syndromes associated with white matter injuries such as periventricular leukomalacia. Infection and inflammation are important risk factors for neonatal brain white matter injuries, but the evaluation of white matter injury in animal models, especially the quantification of myelinated axons, has long been problematic due to the lack of ideal measurement methods. Here, we present an automated segmentation method, which we call MyelinQ, for the quantification of myelinated white matter in immunohistochemical DAB-stained sections of the neonatal mouse brain.

A framework for nucleus and overlapping cytoplasm segmentation in cervical cytology extended depth of field and volume images.

We propose a framework to detect and segment nuclei and segment overlapping cytoplasm in cervical cytology images. This is a challenging task due to folded cervical cells with spurious edges, poor contrast of cytoplasm and presence of neutrophils and artifacts. The algorithm segments nuclei and cell clumps in extended depth of field (EDF) images and uses volume images to segment overlapping cytoplasm.