Automated nuclear segmentation in skin histopathological images using multi-scale radial line scanning

2016 ◽  
Author(s):  
Hongming Xu ◽  
Huiquan Wang ◽  
Richard Berendt ◽  
Naresh Jha ◽  
Mrinal Mandai
Keyword(s):  
Radial Line ◽  
Multi Scale ◽  
Histopathological Images ◽  
Line Scanning ◽  
Nuclear Segmentation

Automatic Nuclear Segmentation Using Multiscale Radial Line Scanning With Dynamic Programming

2017 ◽  
Vol 64 (10) ◽  
pp. 2475-2485 ◽  
Author(s):  
Hongming Xu ◽  
Cheng Lu ◽  
Richard Berendt ◽  
Naresh Jha ◽  
Mrinal Mandal
Keyword(s):  
Dynamic Programming ◽  
Radial Line ◽  
Line Scanning ◽  
Nuclear Segmentation

Detection of melanocytes in skin histopathological images using radial line scanning

2013 ◽  
Vol 46 (2) ◽  
pp. 509-518 ◽  
Author(s):  
Cheng Lu ◽  
Muhammad Mahmood ◽  
Naresh Jha ◽  
Mrinal Mandal
Keyword(s):  
Radial Line ◽  
Histopathological Images ◽  
Line Scanning

scholarly journals Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2031 ◽  
Author(s):  
Taimoor Shakeel Sheikh ◽  
Yonghee Lee ◽  
Migyung Cho
Keyword(s):  
State Of The Art ◽  
Texture Features ◽  
Feature Maps ◽  
Histopathological Classification ◽  
Multi Scale ◽  
Machine Learning Methods ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Histopathological Images

Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability. We propose a multi-scale input and multi-feature network (MSI-MFNet) model, which can learn the overall structures and texture features of different scale tissues by fusing multi-resolution hierarchical feature maps from the network’s dense connectivity structure. The MSI-MFNet predicts the probability of a disease on the patch and image levels. We evaluated the performance of our proposed model on two public benchmark datasets. Furthermore, through ablation studies of the model, we found that multi-scale input and multi-feature maps play an important role in improving the performance of the model. Our proposed model outperformed the existing state-of-the-art models by demonstrating better accuracy, sensitivity, and specificity.

scholarly journals A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images

2021 ◽  
Vol 8 ◽  
Author(s):  
Hongliang He ◽  
Chi Zhang ◽  
Jie Chen ◽  
Ruizhe Geng ◽  
Luyang Chen ◽  
...  
Keyword(s):  
Image Analysis ◽  
State Of The Art ◽  
The State ◽  
Dense Network ◽  
Crucial Step ◽  
Full Resolution ◽  
Proposed Model ◽  
Computer Aided ◽  
Histopathological Images ◽  
Nuclear Segmentation

Nuclear segmentation of histopathological images is a crucial step in computer-aided image analysis. There are complex, diverse, dense, and even overlapping nuclei in these histopathological images, leading to a challenging task of nuclear segmentation. To overcome this challenge, this paper proposes a hybrid-attention nested UNet (Han-Net), which consists of two modules: a hybrid nested U-shaped network (H-part) and a hybrid attention block (A-part). H-part combines a nested multi-depth U-shaped network and a dense network with full resolution to capture more effective features. A-part is used to explore attention information and build correlations between different pixels. With these two modules, Han-Net extracts discriminative features, which effectively segment the boundaries of not only complex and diverse nuclei but also small and dense nuclei. The comparison in a publicly available multi-organ dataset shows that the proposed model achieves the state-of-the-art performance compared to other models.

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