Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Deep learning has become the mainstream technology in computer vision, and it has received extensive research interest in developing new medical image processing algorithms to support disease detection and diagnosis. As compared to conventional machine learning technologies, the major advantage of deep learning is that models can automatically identify and recognize representative features through the hierarchal model architecture, while avoiding the laborious development of hand-crafted features. In this paper, we reviewed and summarized more than 200 recently published papers to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images, which are summarized based on different application scenarios, including lesion classification, segmentation, detection, and image registration. Additionally, we also discussed the major technical challenges and suggested the possible solutions in future research efforts.

Recent advances and clinical applications of deep learning in medical image analysis.

https://hesso.tind.io/record/2362/files/Muller_2018_large_scale_retrieval.pdf

Large-scale retrieval for medical image analytics: A comprehensive review.

Review on deep learning applications in frequency analysis and control of modern power system

/pdf/transforming-medical-imaging-with-transformers-a-comparative-3w3432iy.pdf

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

In the analysis of histopathological images, both holistic (e.g., architecture features) and local appearance features demonstrate excellent performance, while their accuracy may vary dramatically when providing different inputs. This motivates us to investigate how to fuse results from these features to enhance the accuracy. Particularly, we employ content-based image retrieval approaches to discover morphologically relevant images for image-guided diagnosis, using holistic and local features, both of which are generated from the cell detection results by a stacked sparse autoencoder. Because of the dramatically different characteristics and representations of these heterogeneous features (i.e., holistic and local), their results may not agree with each other, causing difficulties for traditional fusion methods. In this paper, we employ a graph-based query-specific fusion approach where multiple retrieval results (i.e., rank lists) are integrated and reordered based on a fused graph. The proposed method is capable of combining the strengths of local or holistic features adaptively for different inputs. We evaluate our method on a challenging clinical problem, i.e., histopathological image-guided diagnosis of intraductal breast lesions, and it achieves $91.67\%$ classification accuracy on $120$ breast tissue images from $40$ patients.

Fusing Heterogeneous Features From Stacked Sparse Autoencoder for Histopathological Image Analysis

We present an algorithm for segmenting a mesh into patches whose boundaries are aligned with prominent ridge and valley lines of the shape. Our key insight is that this problem can be formulated as correlation clustering (CC), a graph partitioning problem originating from the data mining community. The formulation lends two unique advantages to our method over existing segmentation methods. First, since CC is non-parametric, our method has few parameters to tune. Second, as CC is governed by edge weights in the graph, our method offers users direct and local control over the segmentation result. Our technical contributions include the construction of the weighted graph on which CC is defined, a strategy for rapidly computing CC on this graph, and an interactive tool for editing the segmentation. Our experiments show that our method produces qualitatively better segmentations than existing methods on a wide range of inputs.

/pdf/feature-aligned-segmentation-using-correlation-clustering-5f06dgpphh.pdf

Feature-aligned segmentation using correlation clustering

Shadow aliasing due to limited storage precision has been plaguing discrete shadowing algorithms for decades We present a simple method to eliminate false self-shadowing through adaptive depth bias Unlike existing methods which simply set the weight of the bias based on surface slope or utilize the second nearest surface, we evaluate the bound of bias for each fragment and compute the optimal bias within the bound Our method introduces small overhead, preserves more shadow details than widely used constant bias and slope scale bias and works for common 2D shadow maps as well as 3D binary shadow volumes

/pdf/adaptive-depth-bias-for-shadow-maps-35q26lplcd.pdf

Adaptive depth bias for shadow maps

Methods for rendering three-dimensional photo meshes having dynamic content include: (a) detecting a shadow in a three-dimensional photo mesh; (b) removing the shadow from the three-dimensional photo mesh to form a modified photo mesh having a shadow-free texture; (c) simulating a real-time condition in the modified photo mesh; and (d) rendering an image that shows an effect of the real-time condition. Systems for rendering three-dimensional photo meshes having dynamic content are described.

Photorealistic rendering of scenes with dynamic content

Constructing a three dimensional (3D) model of a structure may involve receiving a 3D surface representing a geographic area, the surface having elevation values associated with points of the surface and the geographic area comprises a structure having a geographic footprint smaller than the geographic area. Constructing a 3D model may also involve projecting the elevation values into a two dimensional (2D) plane. Further, a 3D model may be constructed of the structure by assigning model heights based on the elevation values projected into points of the 2D plane.

Structure model creation from a three dimensional surface

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