histological image
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2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Michaela Turčanová ◽  
Martin Hrtoň ◽  
Petr Dvořák ◽  
Kamil Novák ◽  
Markéta Hermanová ◽  
...  

A novel method for semiautomated assessment of directions of collagen fibers in soft tissues using histological image analysis is presented. It is based on multiple rotated images obtained via polarized light microscopy without any additional components, i.e., with just two polarizers being either perpendicular or nonperpendicular (rotated). This arrangement breaks the limitation of 90° periodicity of polarized light intensity and evaluates the in-plane fiber orientation over the whole 180° range accurately and quickly. After having verified the method, we used histological specimens of porcine Achilles tendon and aorta to validate the proposed algorithm and to lower the number of rotated images needed for evaluation. Our algorithm is capable to analyze 5·105 pixels in one micrograph in a few seconds and is thus a powerful and cheap tool promising a broad application in detection of collagen fiber distribution in soft tissues.


2021 ◽  
Author(s):  
Rob F.M. van Doremalen ◽  
Kevin B.W. Groot Lipman ◽  
Esther van 't Riet ◽  
Hans Torrenga ◽  
Maria M. Smits ◽  
...  

Abstract Purpose: The current breast specimen orientation method after breast-conserving surgery is potentially inaccurate due to deformability and mobility of the extracted breast tissue. This complicates targeted relocation during re-excision or radiation. Therefore, we propose a new 3D-visualization method to communicate the breast specimen orientation to instantly provide an intuitive overview of the resection margins in relation to the surgical clips on the wound bed.Methods: In 15 female patients undergoing breast-conserving surgery, the surgeon labeled the surgical clips on the specimen and the wound bed. During pathologic assessment, after inking, a 3D scan was made of the specimen. Tumor tissue was annotated on the histological image and transposed to the respective location inside the 3D model. The transposed resection margins with respect to the labeled surgical clips were calculated and visualized. Intuitivity of the visualization was tested (face validity) as well as the quality of displayed resection margins and labeled clips.Results: Average face validity score for 3D-visualization was between ‘++’ and ‘+’ for surgeons and between ‘+’ and ‘+/-’ for pathologists. Average difference between computed resection margins and reported histologic margins was 1 mm. In 8 cases not all clips could be labeled in situ. In 5 cases not all labeled clips could be retrieved by pathology. Conclusion: The visualizations appeared valuable in interdisciplinary communications. The displayed resection margins approximated the reported margins. Consistent accurate surgical clip labelling proved challenging.


Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1662
Author(s):  
Min-Jen Tsai ◽  
Yu-Han Tao

It is very important to make an objective evaluation of colorectal cancer histological images. Current approaches are generally based on the use of different combinations of textual features and classifiers to assess the classification performance, or transfer learning to classify different organizational types. However, since histological images contain multiple tissue types and characteristics, classification is still challenging. In this study, we proposed the best classification methodology based on the selected optimizer and modified the parameters of CNN methods. Then, we used deep learning technology to distinguish between healthy and diseased large intestine tissues. Firstly, we trained a neural network and compared the network architecture optimizers. Secondly, we modified the parameters of the network layer to optimize the superior architecture. Finally, we compared our well-trained deep learning methods on two different histological image open datasets, which comprised 5000 H&E images of colorectal cancer. The other dataset was composed of nine organizational categories of 100,000 images with an external validation of 7180 images. The results showed that the accuracy of the recognition of histopathological images was significantly better than that of existing methods. Therefore, this method is expected to have great potential to assist physicians to make clinical diagnoses and reduce the number of disparate assessments based on the use of artificial intelligence to classify colorectal cancer tissue.


2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Shiliang Ai ◽  
Chen Li ◽  
Xiaoyan Li ◽  
Tao Jiang ◽  
Marcin Grzegorzek ◽  
...  

Gastric cancer is a common and deadly cancer in the world. The gold standard for the detection of gastric cancer is the histological examination by pathologists, where Gastric Histopathological Image Analysis (GHIA) contributes significant diagnostic information. The histopathological images of gastric cancer contain sufficient characterization information, which plays a crucial role in the diagnosis and treatment of gastric cancer. In order to improve the accuracy and objectivity of GHIA, Computer-Aided Diagnosis (CAD) has been widely used in histological image analysis of gastric cancer. In this review, the CAD technique on pathological images of gastric cancer is summarized. Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. Finally, these techniques are systematically introduced and analyzed for the convenience of future researchers.


2021 ◽  
Author(s):  
Kristopher D McCombe ◽  
Stephanie G Craig ◽  
Amélie Viratham Pulsawatdi ◽  
Javier I Quezada-Marín ◽  
Matthew Hagan ◽  
...  

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. Herein we introduce HistoClean; user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.


2021 ◽  
Author(s):  
Yan Zhang ◽  
Lei Kang ◽  
Xiufeng Li ◽  
Ivy Wong ◽  
Terence Wong

Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursue. Here, we propose a promising and transformative histological imaging method, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP). With the assistance of computational microscopy, CHAMP enables high-throughput and label-free imaging of thick and unprocessed tissues with large surface irregularity at an acquisition speed of 10 mm2/10 seconds with 1.1-um lateral resolution. Moreover, the CHAMP image can be transformed into a virtually stained histological image (Deep-CHAMP) through unsupervised learning within 15 seconds, where significant cellular features are quantitatively extracted with high accuracy. The versatility of CHAMP is experimentally demonstrated using mouse brain/kidney tissues prepared with various clinical protocols, which enables a rapid and accurate intraoperative/postoperative pathological examination without tissue processing or staining, demonstrating its great potential as an assistive imaging platform for surgeons and pathologists to provide optimal adjuvant treatment.


Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 967
Author(s):  
Amirreza Mahbod ◽  
Gerald Schaefer ◽  
Christine Löw ◽  
Georg Dorffner ◽  
Rupert Ecker ◽  
...  

Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository.


2021 ◽  
pp. 1-11
Author(s):  
Yaning Liu ◽  
Lin Han ◽  
Hexiang Wang ◽  
Bo Yin

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.


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