Clumped Nuclei Segmentation with Adjacent Point Match and Local Shape-Based Intensity Analysis in Fluorescence Microscopy Images

Abstract

Highly clumped nuclei captured in fluorescence microscopy images are commonly observed in a wide spectrum of tissue-related biomedical investigations. To ensure the quality of downstream biomedical analyses, it is essential to accurately segment clustered nuclei. However, this presents a technical challenge as fluorescence intensity alone is often insufficient for recovering the true nuclei boundaries. In this paper, we propose an segmentation algorithm that identifies point pair connection candidates and evaluates adjacent point connections with a formulated ellipse fitting quality indicator. After connection relationships are determined, we recover the resulting dividing paths by following points with specific eigenvalues from the image Hessian in a constrained searching space. We validate our algorithm with 560 image patches from two classes of tumor regions of seven brain tumor patients. Both qualitative and quantitative experimental results suggest that our algorithm is promising for dividing overlapped nuclei in fluorescence microscopy images widely used in various biomedical research.

Liver Steatosis Segmentation With Deep Learning Methods

“…4. In columns from left to right, we demonstrate original images, ground-truth, segmentation results from method [6], Mask-RCNN with modified Resnet41, Resnet50, and modified Resnet65, respectively. generate liver steatosis training data after an efficient screening process by a domain expert.…”

“…The method schema is presented in Fig. 1 where three primary components are presented: training data preparation with our prior work on nuclei segmentation [6], model training with transfer learning, and overlapped steatosis segmentation in testing images.…”

“…Representative segmentation results are presented in Fig. 4 where the original input images, ground-truth segmentation, results from our earlier method [6], outputs of presented deep learning approach with modified Resnet41, Resnet50 and modified Resnet65 backbones are presented in columns from left to right. It is noted that deep learning methods, especially the network with backbone Resnet50, present better results than our earlier work as they present less under-segmentation results.…”

“…They are connected based on the fitting quality evaluation by an ellipse fitting model, spatial proximity, shape convexity, and curvature information. Finally, we recover dividing curves by local shape based intensity analysis in a sector-shaped searching space, and produce the corresponding isolated steatosis masks [6].…”

Deep Learning Model for Cell Nuclei Segmentation and Lymphocyte Identification in Whole Slide Histology Images

“…Our study focuses on the customization of cell segmentation autoencoder architecture and aims to investigate a two-step cell segmentation and subsequent lymphocyte classification workflow using digital histology images of H&E stained tumour tissues. Robust separation of clumped cell nuclei is a common challenge in whole slide image analysis (Guo et al, 2018). To tackle this nuclei segmentation challenge, our cell nuclei segmentation model renders an additional active contour layer, which increases the segmentation efficiency of adjacent cell nuclei.…”