The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of varying depths; and (2) their skip connections impose an unnecessarily restrictive fusion scheme, forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks. To overcome these two limitations, we propose UNet++, a new neural architecture for semantic and instance segmentation, by (1) alleviating the unknown network depth with an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision; (2) redesigning skip connections to aggregate features of varying semantic scales at the decoder sub-networks, leading to a highly flexible feature fusion scheme; and (3) devising a pruning scheme to accelerate the inference speed of UNet++. We have evaluated UNet++ using six different medical image segmentation datasets, covering multiple imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and electron microscopy (EM), and demonstrating that (1) UNet++ consistently outperforms the baseline models for the task of semantic segmentation across different datasets and backbone architectures; (2) UNet++ enhances segmentation quality of varying-size objects—an improvement over the fixed-depth U-Net; (3) Mask RCNN++ (Mask R-CNN with UNet++ design) outperforms the original Mask R-CNN for the task of instance segmentation; and (4) pruned UNet++ models achieve significant speedup while showing only modest performance degradation. Our implementation and pre-trained models are available at https://github.com/MrGiovanni/UNetPlusPlus .

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

The present invention provides systems, methods, screens, reagents and kits for optical system analysis of cells to rapidly determine the distribution, environment, or activity of fluorescently labeled reporter molecules in cells for the purpose of screening large numbers of compounds for those that specifically affect particular biological functions.

A system for cell-based screening

A cellular-level study of the pathophysiology is crucial for understanding the mechanisms behind human diseases. Recent advances in quantitative phase imaging (QPI) techniques show promises for the cellular-level understanding of the pathophysiology of diseases. To provide important insight on how the QPI techniques potentially improve the study of cell pathophysiology, here we present the principles of QPI and highlight some of the recent applications of QPI ranging from cell homeostasis to infectious diseases and cancer.

/pdf/quantitative-phase-imaging-techniques-for-the-study-of-cell-2vlo97gmxz.pdf

Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications.

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Boosting: Foundations and Algorithms.

Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

A free-lying cell classifier. An automated microscope system comprising a computer and high speed processing field of view processors identifies free-lying cells. An image of a biological specimen is obtained and the image is segmented to create a set of binary masks. The binary masks are used by a feature calculator to compute the features that characterize objects of interest including free-lying cells, artifacts and other biological objects. The objects are classified to identify their type, their normality or abnormality or their identification as an artifact. The results are summarized and reported. A stain evaluation of the slide is performed as well as a typicality evaluation. The robustness of the measurement is also quantified as a classification confidence value. The free-lying cell evaluation is used by an automated cytology system to classify a biological specimen slide.

Apparatus for the identification of free-lying cells

The detection of cellular aggregates within cytologic samples. An image analysis system with an image gathering system (511) includes a camera (512), a motion controller (520), an illumination system (508) and an interface obtains images (55) of cell groupings. The image gathering system (511) is constructed for gathering image data (38) of a specimen mounted on a slide and is coupled to a data processing system (540). Image data (38) is transferred from the image gathering system (511) to the data processing system (540). The data processing system (540) obtains objects of interest. A four step process finds cellular aggregates. The first step is acquisition of an image for analysis (12). The second step is extraction of image features (14). The third step is classification of the image (16) to determine if any potential cellular aggregates may exist in the image (24). The fourth step is segmentation of objects (18) which includes the substeps of detecting and locating potential cellular aggregates.

Apparatus for automated identification of thick cell groupings on a biological specimen

A biological specimen classification strategy employs identification and integration of multiple cell patterns. An automated microscope acquires an image of a biological specimen such as a Pap smear and provides an image output to biological classifiers. The classifiers independently detect and classify a number of specimen types and provide classifications to an output field of view integrator. The integrator integrates the classifications. The integrated output then determines whether the classifiers should be reapplied to the image.

Apparatus for identification and integration of multiple cell patterns

Automated cell analysis in 2D and 3D: A comparative study

The invention detects areas of interest at low magnification, locating possible abnormal cells or other cells of interest using image processing and statistical pattern recognition techniques. Next, at high magnification, the areas identified at low magnification are re-examined. The information from the low magnification and high magnification scans is collated and a determination is made about the slide--whether it is normal, abnormal, contains endocervical component, and so forth. The invention also provides a method and apparatus to train object feature and slide feature classifiers. The invention provides an automated cytology system that can process training slides for use in a feed back classifier development environment. The invention also can classify endocervical groups of cells.

Cytological slide scoring apparatus

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