Cell‐type based semantic segmentation of histopathological images using deep convolutional neural networks

2019 ◽  
Vol 29 (3) ◽  
pp. 234-246 ◽  
Author(s):  
Şaban Öztürk ◽  
Bayram Akdemir
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Cell Type ◽  
Deep Convolutional Neural Networks ◽  
Histopathological Images

scholarly journals Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 427 ◽  
Author(s):  
Sanxing Zhang ◽  
Zhenhuan Ma ◽  
Gang Zhang ◽  
Tao Lei ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Input Image ◽  
Feature Representation ◽  
Segmentation Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Semantic Image Segmentation

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

scholarly journals A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew Lagree ◽  
Majidreza Mohebpour ◽  
Nicholas Meti ◽  
Khadijeh Saednia ◽  
Fang-I. Lu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Breast Tissue ◽  
Superior Performance ◽  
Histopathological Analysis ◽  
Cell Nuclei ◽  
Deep Convolutional Neural Networks ◽  
Nuclei Segmentation ◽  
Histopathological Images ◽  
Biopsy Examination

AbstractBreast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented.

scholarly journals Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images

2019 ◽  
Vol 6 (02) ◽  
pp. 1 ◽  
Author(s):  
Farhad Ghazvinian Zanjani ◽  
Svitlana Zinger ◽  
Bastian Piepers ◽  
Saeed Mahmoudpour ◽  
Peter Schelkens
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cancer Detection ◽  
Metastatic Cancer ◽  
Jpeg 2000 ◽  
Deep Convolutional Neural Networks ◽  
Histopathological Images
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