142 research outputs found
Developing the Quantitative Histopathology Image Ontology : A case study using the hot spot detection problem
Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology â QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
Guest Editorial to the Special Letters Issue on Emerging Technologies in Multiparameter Biomedical Optical Imaging and Image Analysis
The past two decades have witnessed revolutionary advances
in biomedical imaging modalities capable of providing
biological and physiological information from the cellular
scale to the organ level. Recent advances have also been
focused on cost-effective, noninvasive, portable, and molecularimaging
technologies for imaging at microscopic, mesoscopic,
and macroscopic levels. These technologies have significant
potential to advance biomedical research and clinical practice.
They can also provide a better understanding and monitoring
of physiological and functional disorders, which could lead to
mainstream diagnostic technologies of the future
Image Analysis for Cystic Fibrosis: Computer-Assisted Airway Wall and Vessel Measurements from Low-Dose, Limited Scan Lung CT Images
Cystic fibrosis (CF) is a life-limiting genetic disease that affects approximately 30,000 Americans. When compared to those of normal children, airways of infants and young children with CF have thicker walls and are more dilated in high-resolution computed tomographic (CT) imaging. In this study, we develop computer-assisted methods for assessment of airway and vessel dimensions from axial, limited scan CT lung images acquired at low pediatric radiation doses. Two methods (threshold- and model-based) were developed to automatically measure airway and vessel sizes for pairs identified by a user. These methods were evaluated on chest CT images from 16 pediatric patients (eight infants and eight children) with different stages of mild CF related lung disease. Results of threshold-based, corrected with regression analysis, and model-based approaches correlated well with both electronic caliper measurements made by experienced observers and spirometric measurements of lung function. While the model-based approach results correlated slightly better with the human measurements than those of the threshold method, a hybrid method, combining these two methods, resulted in the best results
Analysis of temporal changes of mammographic features: Computerâ aided classification of malignant and benign breast masses
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135117/1/mp2242.pd
Morphological subband decomposition structure using GF(N) arithmetic
Linear filter banks with critical subsampling and perfect reconstruction (PR) property have received much interest and found numerous applications in signal and image processing. Recently, nonlinear filter bank structures with PR and critical subsampling have been proposed and used in image coding. In this paper, it is shown that PR nonlinear subband decomposition can be performed using the Gallois Field (GF) arithmetic. The result of the decomposition of an n-ary (e.g. 256-ary) input signal is still n-ary at different resolutions. This decomposition structure can be utilized for binary and 2k (k is an integer) level signal decompositions. Simulation studies are presented
Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images
Although multiple instance learning (MIL) methods are widely used for
automatic tumor detection on whole slide images (WSI), they suffer from the
extreme class imbalance within the small tumor WSIs. This occurs when the tumor
comprises only a few isolated cells. For early detection, it is of utmost
importance that MIL algorithms can identify small tumors, even when they are
less than 1% of the size of the WSI. Existing studies have attempted to address
this issue using attention-based architectures and instance selection-based
methodologies, but have not yielded significant improvements. This paper
proposes cross-attention-based salient instance inference MIL (CASiiMIL), which
involves a novel saliency-informed attention mechanism, to identify breast
cancer lymph node micro-metastasis on WSIs without the need for any
annotations. Apart from this new attention mechanism, we introduce a negative
representation learning algorithm to facilitate the learning of
saliency-informed attention weights for improved sensitivity on tumor WSIs. The
proposed model outperforms the state-of-the-art MIL methods on two popular
tumor metastasis detection datasets, and demonstrates great cross-center
generalizability. In addition, it exhibits excellent accuracy in classifying
WSIs with small tumor lesions. Moreover, we show that the proposed model has
excellent interpretability attributed to the saliency-informed attention
weights. We strongly believe that the proposed method will pave the way for
training algorithms for early tumor detection on large datasets where acquiring
fine-grained annotations is practically impossible
Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries
This study explored the potential of gait analysis as a tool for assessing
post-injury complications, e.g., infection, malunion, or hardware irritation,
in patients with lower extremity fractures. The research focused on the
proficiency of supervised machine learning models predicting complications
using consecutive gait datasets. We identified patients with lower extremity
fractures at an academic center. Patients underwent gait analysis with a
chest-mounted IMU device. Using software, raw gait data was preprocessed,
emphasizing 12 essential gait variables. Machine learning models including
XGBoost, Logistic Regression, SVM, LightGBM, and Random Forest were trained,
tested, and evaluated. Attention was given to class imbalance, addressed using
SMOTE. We introduced a methodology to compute the Rate of Change (ROC) for gait
variables, independent of the time difference between gait analyses. XGBoost
was the optimal model both before and after applying SMOTE. Prior to SMOTE, the
model achieved an average test AUC of 0.90 (95% CI: [0.79, 1.00]) and test
accuracy of 86% (95% CI: [75%, 97%]). Feature importance analysis attributed
importance to the duration between injury and gait analysis. Data patterns
showed early physiological compensations, followed by stabilization phases,
emphasizing prompt gait analysis. This study underscores the potential of
machine learning, particularly XGBoost, in gait analysis for orthopedic care.
Predicting post-injury complications, early gait assessment becomes vital,
revealing intervention points. The findings support a shift in orthopedics
towards a data-informed approach, enhancing patient outcomes.Comment: 13 pages, 6 figure
Guest Editorial to the Special Letters Issue on Emerging Technologies in Multiparameter Biomedical Optical Imaging and Image Analysis
The past two decades have witnessed revolutionary advances
in biomedical imaging modalities capable of providing
biological and physiological information from the cellular
scale to the organ level. Recent advances have also been
focused on cost-effective, noninvasive, portable, and molecularimaging
technologies for imaging at microscopic, mesoscopic,
and macroscopic levels. These technologies have significant
potential to advance biomedical research and clinical practice.
They can also provide a better understanding and monitoring
of physiological and functional disorders, which could lead to
mainstream diagnostic technologies of the future
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