With applications in object detection, image feature extraction, image classification, and
image segmentation, artificial intelligence is facilitating high-throughput analysis of image …

In many computerized methods for cell detection, segmentation, and classification in digital
histopathology that have recently emerged, the task of cell segmentation remains a chief …

Microscopy images are rich in information about the dynamic relationships among biological
structures. However, extracting this complex information can be challenging, especially …

Object segmentation and structure localization are important steps in automated image
analysis pipelines for microscopy images. We present a convolution neural network (CNN) …

High-resolution microscopy images of tissue specimens provide detailed information about
the morphology of normal and diseased tissue. Image analysis of tissue morphology can …

Histopathology is diagnosis based on visual examination of tissue sections under a
microscope. With the growing number of digitally scanned tissue slide images, computer …

Computerized microscopy image analysis plays an important role in computer aided
diagnosis and prognosis. Machine learning techniques have powered many aspects of …

Motivation Deep learning use for quantitative image analysis is exponentially increasing.
However, training accurate, widely deployable deep learning algorithms requires a plethora …

Highlights•Adoption of segmentation models driven by user-friendly tools and
documentation.•Advancements in novel architectures are driven by the need for a truly …

With recent advances in high-throughput, automated microscopy, there has been an
increased demand for effective computational strategies to analyze large-scale, image …