scholarly journals Advances in digital pathology

2018 ◽  
Vol 1 (2) ◽  
pp. 33-39
Author(s):  
Evgin Goceri
Keyword(s):  
Digital Pathology ◽  
Computer Assisted ◽  
Cell Detection ◽  
Tissue Samples ◽  
Nuclei Segmentation ◽  
New Ideas ◽  
Assisted Diagnosis ◽  
Tissue Specimens ◽  
Validation Of Results

Characterization of cancer diseases and preparation of diagnostic reports after analyzing tissue specimens and several cell samples are provided by pathologists. One of the most successful strategies in pathology is to divide tumors into different subtypes and to adapt the treatment for each tumor. However, this approach has put a big burden on pathologists, who are reviewing tissue samples under the light of the microscope. Because, tumors have about 200 subtypes and pathologies are facing a growing demand for accurate and fast diagnosis and also patient safety. Therefore, digital pathology has been important and growing rapidly. Advances in computer technology such as computing power, faster networks and cheaper storage have enabled pathologists to manage images more easily than in the last decade. Novel pathology tools have a potential for automated and faster diagnosis and also better management of data. Moreover, it enables re-reducibility, validation of results, quality assurance and sharing of new ideas at anywhere and anytime. Advances in digital pathology have been reviewed in this paper. It seems that innovations in technologies will not only provide important improvements in pathology service, but also they will change healthcare and research fundamentally despite some challenges.   Keywords: Cell detection, computer assisted diagnosis, digital pathology, image analysis, nuclei segmentation, tissue classification.          

Quantitative Characterization of Mass Lesions on Digitized Mammograms for Computer-Assisted Diagnosis

2000 ◽  
Vol 35 (6) ◽  
pp. 366-372 ◽  
Author(s):  
ISAAC LEICHTER ◽  
SHALOM BUCHBINDER ◽  
PHILIPPE BAMBERGER ◽  
BORIS NOVAK ◽  
SCOTT FIELDS ◽  
...  
Keyword(s):  
Computer Assisted ◽  
Quantitative Characterization ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis ◽  
Mass Lesions

scholarly journals A Segmentation-Free Machine Learning Architecture for Immune Landscape Phenotyping in Solid Tumors by Multichannel Imaging

2021 ◽  
Author(s):  
Shabaz Sultan ◽  
Mark A. J. Gorris ◽  
Lieke L. van der Woude ◽  
Franka Buytenhuijs ◽  
Evgenia Martynova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid Tumors ◽  
Immune Cells ◽  
Multispectral Imaging ◽  
Immune Cell ◽  
Cell Detection ◽  
Tissue Samples ◽  
Primary Tumors ◽  
Tissue Specimens ◽  
Free Machine

Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treatment of cancer patients. Multispectral imaging allows in situ visualization of heterogeneous cell subsets, such as lymphocytes, in tissue samples. Many image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments such as solid tumors, segmentation-based phenotyping can be inaccurate due to segmentation errors or overlapping cell boundaries. Here we introduce a machine learning pipeline design called ImmuNet that directly identifies the positions and phenotypes of immune cells without determining their exact boundaries. ImmuNet is easy to train: human annotators only need to click on immune cells and rank their expression of each marker; full annotation of tissue regions is not necessary. We demonstrate that ImmuNet is a suitable approach for immune cell detection and phenotyping in multiplex immunohistochemistry: it compares favourably to segmentation-based methods, especially in dense tissues, and we externally validate ImmuNet results by comparing them to flow cytometric measurements from the same tissue. In summary, ImmuNet performs well on diverse tissue specimens, takes relatively little effort to train and implement, and is a simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes are required for downstream analyses. We hope that ImmuNet will help cancer researchers to analyze multichannel tissue images more easily and accurately.

scholarly journals The value of GATA6 immunohistochemistry and computer-assisted diagnosis to predict clinical outcome in advanced pancreatic cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Duan ◽  
Gun-Ho Jang ◽  
Robert C. Grant ◽  
Julie M. Wilson ◽  
Faiyaz Notta ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Open Source Software ◽  
Digital Pathology ◽  
Advanced Pancreatic Cancer ◽  
Molecular Data ◽  
Ductal Adenocarcinoma ◽  
Computer Assisted ◽  
Biopsy Specimens ◽  
Assisted Diagnosis

AbstractCombination chemotherapy, either modified FOLFIRINOX (mFFX) or gemcitabine–nabpaclitaxel, are used in the treatment of most patients with advanced pancreatic ductal adenocarcinoma (PDAC), yet robust biomarkers of outcome are currently lacking to guide regimen selection. Here, we tested GATA6 immunohistochemistry (IHC) as a putative biomarker in advanced PDAC. GATA6 is a transcription factor in normal pancreas development. Two pathologists, blinded to clinical and molecular data, independently assessed GATA6 IHC in biopsy specimens of 130 patients with advanced PDAC, in 2 distinct phases (without and with computer assistance using the open source software QuPath). Low GATA6 IHC expression was associated with shorter overall survival [median OS 6.2 months for patients with GATA6 low tumors vs. 11.5 months for patients with GATA6 high tumors, HR 1.66 (95% CI 1.15–2.40), P = 0.007]. Progression appears to be higher in GATA6-low tumors compared to GATA6-high tumors in patients treated with mFFX (P = 0.024) but not in patients treated with gemcitabine regimens. GATA6 IHC expression was significantly associated with molecular subtypes (P = 0.0003). Digital assistance markedly improved interrater concordance (Cohen’s kappa scores of 0.32 vs. 0.95). Our results provide strong evidence that GATA6 IHC can be used as a single biomarker in the clinic to predict clinical outcome in advanced PDAC, warranting further investigation in prospective clinical trials. These results provide the basis for an improved classification of PDAC and future biomarker design using digital pathology workflow.

scholarly journals Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte

Revista Med ◽  
2014 ◽  
Vol 22 (2) ◽  
pp. 79 ◽  
Author(s):  
John Arevalo ◽  
Angel Cruz-Roa ◽  
Fabio A. González O
Keyword(s):  
Image Analysis ◽  
Image Representation ◽  
Digital Pathology ◽  
Virtual Microscopy ◽  
Automatic Analysis ◽  
Computer Assisted ◽  
Automatic Image Analysis ◽  
Histopathology Images ◽  
Image Enhancing ◽  
Assisted Diagnosis

<p>This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology.</p>

Characterization of Colorectal Adenocarcinoma Sections by Spatially Resolved FT-IR Microspectroscopy

2002 ◽  
Vol 56 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Peter Lasch ◽  
Wolfgang Haensch ◽  
E. Neil Lewis ◽  
Linda H. Kidder ◽  
Dieter Naumann
Keyword(s):  
Colorectal Adenocarcinoma ◽  
Clinical Analysis ◽  
Tissue Samples ◽  
Spatially Resolved ◽  
Ft Ir ◽  
Ir Microspectroscopy ◽  
Artificial Neural ◽  
Tissue Specimens ◽  
Spectral Databases

A combination of Fourier transform infrared (FT-IR) spectroscopy and microscopy, FT-IR microspectroscopy, has been used to characterize sections of human colorectal adenocarcinoma. In this report, a database of 2601 high quality FT-IR point spectra from 26 patient samples and seven different histological structures was recorded and analyzed. The computer-based analysis of the IR spectra was carried out in four steps: (1) an initial test for spectral quality, (2) data pre-processing, (3) data reduction and feature selection, and (4) classification of the tissue spectra by multivariate pattern recognition techniques such as hierarchical clustering and artificial neural network analysis. Furthermore, an example of how spectral databases can be utilized to reassemble false color images of tissue samples is presented. The overall classification accuracy attained by optimized artificial neural networks reached 95%, highlighting the great potential of FT-IR microspectroscopy as a potentially valuable, reagent-free technique for the characterization of tissue specimens. However, technical improvements and the compilation of validated spectral databases are essential prerequisites to make the infrared technique applicable to routine and experimental clinical analysis.

scholarly journals Aggregation-and-Attention Network for brain tumor segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chih-Wei Lin ◽  
Yu Hong ◽  
Jinfu Liu
Keyword(s):  
Brain Tumor ◽  
Semantic Information ◽  
Spatial Relationship ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Assisted Diagnosis ◽  
The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

scholarly journals Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps

2021 ◽  
Author(s):  
Ali H. Al-Timemy ◽  
Nebras H. Ghaeb ◽  
Zahraa M. Mosa ◽  
Javier Escudero
Keyword(s):  
Transfer Learning ◽  
Learning Strategy ◽  
Clinical Decision ◽  
Topographic Maps ◽  
Computer Assisted ◽  
Clinical Knowledge ◽  
Average Accuracy ◽  
Assisted Diagnosis ◽  
Improved Accuracy ◽  
Fine Tune

Abstract Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision based on the fusion of probabilities. Individually, the classifier based on PI achieved 93.1% accuracy, whereas the deep classifiers reached classification accuracies over 90% only in isolated cases. Overall, the average accuracy of the deep networks over the four corneal maps ranged from 86% (SfN) to 89.9% (AN). The classifier ensemble increased the accuracy of the deep classifiers based on corneal maps to values ranging (92.2% to 93.1%) for SqN and (93.1% to 94.8%) for AN. Including in the ensemble-specific combinations of corneal maps’ classifiers and PI increased the accuracy to 98.3%. Moreover, visualization of first learner filters in the networks and Grad-CAMs confirmed that the networks had learned relevant clinical features. This study shows the potential of creating ensembles of deep classifiers fine-tuned with a transfer learning strategy as it resulted in an improved accuracy while showing learnable filters and Grad-CAMs that agree with clinical knowledge. This is a step further towards the potential clinical deployment of an improved computer-assisted diagnosis system for KCN detection to help ophthalmologists to confirm the clinical decision and to perform fast and accurate KCN treatment.

scholarly journals Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

2017 ◽  
Vol 30 (6) ◽  
pp. 796-811 ◽  
Author(s):  
Afsaneh Jalalian ◽  
Syamsiah Mashohor ◽  
Rozi Mahmud ◽  
Babak Karasfi ◽  
M. Iqbal Saripan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

scholarly journals Demonstration of an integrated nanophotonic chip-scale alkali vapor magnetometer using inverse design

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yoel Sebbag ◽  
Eliran Talker ◽  
Alex Naiman ◽  
Yefim Barash ◽  
Uriel Levy
Keyword(s):  
Spatial Resolution ◽  
Near Field ◽  
Computer Assisted ◽  
Experimental Characterization ◽  
Optical Isolation ◽  
New Concepts ◽  
On Chip ◽  
The Cost ◽  
Micrometer Scale

AbstractRecently, there has been growing interest in the miniaturization and integration of atomic-based quantum technologies. In addition to the obvious advantages brought by such integration in facilitating mass production, reducing the footprint, and reducing the cost, the flexibility offered by on-chip integration enables the development of new concepts and capabilities. In particular, recent advanced techniques based on computer-assisted optimization algorithms enable the development of newly engineered photonic structures with unconventional functionalities. Taking this concept further, we hereby demonstrate the design, fabrication, and experimental characterization of an integrated nanophotonic-atomic chip magnetometer based on alkali vapor with a micrometer-scale spatial resolution and a magnetic sensitivity of 700 pT/√Hz. The presented platform paves the way for future applications using integrated photonic–atomic chips, including high-spatial-resolution magnetometry, near-field vectorial imaging, magnetically induced switching, and optical isolation.

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