This work was supported by grants from the Collaborative Research and Training Experience Programme (CREATE) and a Discovery Grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada.

The authors have nothing to disclose.

We have presented a series of sophisticated techniques of digital image processing and pattern recognition, also known as machine learning and CAD, for the detection of architectural distortion in prior mammograms of interval-cancer cases. The methods are based on analysis of the oriented textural patterns present in mammographic images. Our methods, including several more features proposed in our related works, are capable of detecting early signs of breast cancer 15 months ahead of the time of clinical diagnosis, on th...

Breast cancer is a major disease affecting women and is the second leading cause of cancer related death among women 1,2. In order to improve the chance of survival and the prognosis of the affected patients through effective treatment at early stages of breast cancer, the disease needs to be detected as early as possible. In retrospective analysis of cases of breast cancer, subtle signs of abnormalities have been observed on previously acquired screening mammograms 3,4. Architectural distortion is one such localized mammographic sign of possibly early stages of breast cancer that is difficult to detect 5,6. The associated patterns are vaguely described as distortion of the normal architecture of the breast with no definite mass visible. Architectural distortion could appear at the initial stages of the formation of a breast mass or tumor. We hypothesize that screening mammograms obtained prior to the detection of breast cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion.
Figure 1a shows a prior mammographic image of a case of screen-detected cancer. The region of abnormality identified by a radiologist (J.E.L.D.) is outlined with a red rectangle. The prior mammogram was taken 24 months before the detection mammogram shown in Figure 1b. The prior mammogram had been declared to be free of signs of cancer at the original instance of screening. In retrospective analysis and in comparison with the detection mammogram, a suspicious region related to the site of cancer detected was labeled by the radiologist, and is outlined in red on the prior mammogram. The suspicious region contains signs of architectural distortion, including spicules.
Computer-aided diagnosis (CAD) techniques and systems offer the potential for achieving increased sensitivity in the detection of breast cancer 2,7-9. However, in comparison with the number of publications that exist in the literature on the detection of other signs of breast cancer, such as masses and calcifications, only a small number of studies have been reported on the detection of architectural distortion in the absence of a central mass 10-17. Commercially available CAD systems have been found to perform poorly in the detection of architectural distortion 18. Studies on the detection of architectural distortion in prior mammograms of screen-detected or interval-cancer cases 3,4,19-22 could help in developing strategies for the detection and treatment of breast diseases at their early stages, and lead to improvement in the prognosis for the patient 23.
Preparation of Images for the Experiment
Experiments were conducted with 158 mammographic images including 106 prior mammograms of 56 individuals diagnosed with breast cancer and 52 images of 13 normal individuals. Ethics approval for the study was obtained from the Conjoint Health Research Ethics Board, Office of Medical Bioethics, University of Calgary, and the Calgary Regional Health Authority. The images were obtained from Screen Test: Alberta Program for the Early Detection of Breast Cancer 21,24,25.
Mammograms acquired in the last scheduled visit to the screening program prior to the diagnosis of cancer outside the screening program were labeled as prior mammograms of interval-cancer cases. The corresponding diagnostic mammograms were not available. All but two of the 106 prior mammograms had been declared to be free of any sign of breast cancer at the time of their acquisition and analysis in the screening program; the individuals corresponding to the other two mammograms had been referred for biopsy. The time interval between the diagnosis of cancer and prior mammograms ranged from 1.5 months to 24.5 months, with an average of 15 months and standard deviation of 7 months. All of the prior mammograms of interval-cancer cases available in the database have been included in the present study, except six images in which no suspicious parts could be identified.
The screen-film mammograms were digitized at the spatial resolution of 50 &mu;m and gray-scale resolution of 12 bits per pixel using the Lumiscan 85 laser scanner (Lumisys, Sunnyvale, CA). An expert radiologist specialized in mammography (J.E.L.D.) reviewed all of the 106 prior mammograms of interval-cancer cases and marked the suspected regions of architectural distortion with rectangular boxes based on the reports available on subsequent imaging or biopsy, or by detailed inspection of the mammograms. Of the 106 prior mammographic images in the dataset used in the present study, 38 images have visible architectural distortion, and the remaining 68 images contain questionable or no clearly evident architectural distortion. Each prior mammogram contains a single site of architectural distortion as identified by the rectangular box drawn by the radiologist. The average width, height, and area of the 106 suspicious parts of images marked by the radiologist are 56 mm, 39 mm, and 2,274 mm2, with standard deviation of 11.8 mm, 11.6 mm, and 1073.9 mm2, respectively.

detection of architectural distortion in prior mammograms via analysis of oriented patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion.
Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.

The three features, namely, node value, FD, and HF , provided AUC values of 0.61, 0.59, and 0.64, respectively, when each feature was used on its own. Combined use of the three features provided improved performance with AUC = 0.70. The FROC curve obtained with the combination of the three features is shown in Figure 11, which indicates a sensitivity of 80% at 5.6 FPs/patient and 89% at 7.5 FPs/patient. Use of only the node value provided a sensitivity of 80% at 8.1 FPs/patient and 89...

Watch this Scientific Journal Video about Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns at JoVE.com

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms. Oriented structures are analyzed using Gabor filters and phase portraits to detect sites of radiating tissue patterns. Each site is characterized and classified using measures to represent spiculating patterns. The methods should assist in the detection of breast cancer.

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of ...

detection-architectural-distortion-prior-mammograms-via-analysis

Universidad de Cartagena UDEC

Research

JoVE Journal

Medicine

42.6K Views.  University of Calgary. We demonstrate methods for the detection of architectural distortion in prior mammograms. Oriented structures are analyzed using Gabor filters and phase portraits to detect sites of radiating tissue patterns. Each site is characterized and classified using measures to represent spiculating patterns. The methods should assist in the detection of breast cancer.

Video: Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to the site of inflammation and are activated to prevent a further spreading of the invasion. This is also reflected by changes in the behavior and the morphological appearance of the immune cells. In cancerous tissue, similar morphokinetic changes have been observed in the behavior of microglial cells: intra-tumoral microglia have less complex 3-dimensional shapes, having less-branched cellular processes, and move more rapidly than those in healthy tissue. The examination of such morphokinetic properties requires complex 3D microscopy techniques, which can be extremely challenging when executed longitudinally. Therefore, the recording of a static 3D shape of a cell is much simpler, because this does not require intravital measurements and can be performed on excised tissue as well. However, it is essential to possess analysis tools that allow the fast and precise description of the 3D shapes and allows the diagnostic classification of healthy and pathogenic tissue samples based solely on static, shape-related information. Here, we present a toolkit that analyzes the discrete Fourier components of the outline of a set of 2D projections of the 3D cell surfaces via Self-Organizing Maps. The application of artificial intelligence methods allows our framework to learn about various cell shapes as it is applied to more and more tissue samples, whilst the workflow remains simple.

Here, we provide a workflow that allows the identification of healthy and pathological cells based on their 3-dimensional shape. We describe the process of using 2D projection outlines based on the 3D surfaces to train a Self-Organizing Map that will provide objective clustering of the investigated cell populations.

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to ...

Cancer Research

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

Measuring the Complete-arch Distortion of an Optical Dental Impression

Digital workflows have actively been used to produce dental restorations or oral appliances since dentists started to make digital impressions by acquiring 3D images with an intraoral scanner. Because of the nature of scanning the oral cavity in the patient&#39;s mouth, the intraoral scanner is a handheld device with a small optical window, stitching together small data to complete the entire image. During the complete-arch impression procedure, a deformation of the impression body can occur and affect the fit of the restoration or appliance. In order to measure these distortions, a master specimen was designed and produced with a metal 3D printer. Designed reference geometries allow setting independent coordinate systems for each impression and measure x, y, and z displacements of the cylinder top circle center where the distortion of the impression can be evaluated. In order to evaluate the reliability of this method, the coordinate values of the cylinder are calculated and compared between the original computer-aided design (CAD) data and the reference data acquired with the industrial scanner. The coordinate differences between the two groups were mostly less than 50 &#181;m, but the deviations were high due to the tolerance of 3D printing in the z coordinates of the obliquely designed cylinder on the molar. However, since the printed model sets a new standard, it does not affect the results of the test evaluation. The reproducibility of the reference scanner is 11.0 &#177; 1.8 &#181;m. This test method can be used to identify and improve upon the intrinsic problems of an intraoral scanner or to establish a scanning strategy by measuring the degree of distortion at each part of the complete-arch digital impression.

Here, we present a protocol to measure the degree of distortion at each part of the compete-arch digital impression acquired from an intraoral scanner with 3D-printed metal phantom with standard geometries.

Digital workflows have actively been used to produce dental restorations or oral appliances since dentists started to make digital impressions by ...

Engineering

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). 
The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. 
The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.

We describe how to obtain parametric and vector maps of the diffusion tensor of the breast using magnetic resonance imaging. The protocol and final output following imaging processing are tailored for tracking breast architectural features and detecting breast malignancy.

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of ...

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

We have developed a calibration target for use with fluid-immersed endoscopes within the context of the GIFT-Surg (Guided Instrumentation for Fetal Therapy and Surgery) project. One of the aims of this project is to engineer novel, real-time image processing methods for intra-operative use in the treatment of congenital birth defects, such as spina bifida and the twin-to-twin transfusion syndrome. The developed target allows for the sterility-preserving optical distortion calibration of endoscopes within a few minutes. Good optical distortion calibration and compensation are important for mitigating undesirable effects like radial distortions, which not only hamper accurate imaging using existing endoscopic technology during fetal surgery, but also make acquired images less suitable for potentially very useful image computing applications, like real-time mosaicing. In this paper proposes a novel fabrication method to create an affordable, sterilizable calibration target suitable for use in a clinical setup. This method involves etching a calibration pattern by laser cutting a sandblasted stainless steel sheet. This target was validated using the camera calibration module provided by OpenCV, a state-of-the-art software library popular in the computer vision community.

This article describes the design and development of a sterilizable custom camera optical distortion calibration target for the peri-operative, fluid-immersed calibration of endoscopes during endoscopic interventions.

We have developed a calibration target for use with fluid-immersed endoscopes within the context of the GIFT-Surg (Guided Instrumentation for Fetal ...

Bioengineering

Analysis of Craniomaxillofacial Malformations in Mice Using Three-dimensional Microcomputed Tomography

To model craniofacial malformations caused by vitamin A deficiency (VAD), we expressed a dominant-negative retinoid receptor mutation in osteoblasts to specifically inhibit RAR transcriptional activity in mice. This approach allowed us to investigate the effects of VAD on cranial hypomineralization, mandibular deformity, and clavicular hypoplasia in clinical cases. In this study, microcomputed tomography (microCT) scanning of the craniomaxillofacial region of mice represented a valuable tool for studying the growth and development of this animal model. The manual estimation of images is both time-consuming and inaccurate. Hence, here, we present a straightforward, efficient, and accurate approach for segmenting and quantifying the microCT images of each craniomaxillofacial bone. MicroCT software was used to slice the mandible, frontal bone, parietal bone, nasal bone, premaxilla, maxilla, interparietal bone, and occipital bone of mice and measure their corresponding lengths and widths. This segmentation method can be applied to study growth and development in developmental biology, biomedicine, and other related sciences and allows researchers to analyze the effects of genetic mutations on individual craniofacial bones.

Here, we describe in depth a method, which is based on microcomputed tomography, to segment and measure 3D models of craniomaxillofacial bones in mice, for better assessment of craniomaxillofacial bone development in mice than what is possible with current methods.

To model craniofacial malformations caused by vitamin A deficiency (VAD), we expressed a dominant-negative retinoid receptor mutation in osteoblasts to ...

Developmental Biology

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Histology volume reconstruction facilitates the study of 3D shape and volume change of an organ at the level of macrostructures made up of cells. It can also be used to investigate and validate novel techniques and algorithms in volumetric medical imaging and therapies. Creating 3D high-resolution atlases of different organs1,2,3 is another application of histology volume reconstruction. This provides a resource for investigating tissue structures and the spatial relationship between various cellular features. We present an image registration approach for histology volume reconstruction, which uses a set of optical blockface images. The reconstructed histology volume represents a reliable shape of the processed specimen with no propagated post-processing registration error. The Hematoxylin and Eosin (H&#38;E) stained sections of two mouse mammary glands were registered to their corresponding blockface images using boundary points extracted from the edges of the specimen in histology and blockface images. The accuracy of the registration was visually evaluated. The alignment of the macrostructures of the mammary glands was also visually assessed at high resolution.
This study delineates the different steps of this image registration pipeline, ranging from excision of the mammary gland through to 3D histology volume reconstruction. While 2D histology images reveal the structural differences between pairs of sections, 3D histology volume provides the ability to visualize the differences in shape and volume of the mammary glands.

We present an image registration approach for 3-dimensional (3D) histology volume reconstruction, which facilitates the study of the changes of an organ at the level of macrostructures made up of cells . Using this approach, we studied the 3D changes between wild-type and Igfbp7-null mammary glands.

Histology volume reconstruction facilitates the study of 3D shape and volume change of an organ at the level of macrostructures made up of cells. It can ...

Biology

3D Reconstruction for the Diagnosis and Treatment of Pulmonary Nodules

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating pulmonary nodules, and these approaches are progressively being acknowledged and adopted by physicians and patients. Nonetheless, constructing a relatively universal 3D digital model of pulmonary nodules for diagnosis and treatment is challenging due to device differences, shooting times, and nodule types. The objective of this study is to propose a new 3D digital model of pulmonary nodules that serves as a bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation. Many AI-driven pulmonary nodule detection and recognition methods employ deep learning techniques to capture the radiological features of pulmonary nodules, and these methods can achieve a good area under-the-curve (AUC) performance. However, false positives and false negatives remain a challenge for radiologists and clinicians. The interpretation and expression of features from the perspective of pulmonary nodule classification and examination are still unsatisfactory. In this study, a method of continuous 3D reconstruction of the whole lung in horizontal and coronal positions is proposed by combining existing medical image processing technologies. Compared with other applicable methods, this method allows users to rapidly locate pulmonary nodules and identify their fundamental properties while also observing pulmonary nodules from multiple perspectives, thereby providing a more effective clinical tool for diagnosing and treating pulmonary nodules.

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules  

The objective of this study is to develop a novel 3D digital model of pulmonary nodules that serves as a communication bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation.

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating ...

3D Cephalometric Landmark Annotation Demonstration on Human CBCT Scans

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Craniofacial cephalometric analysis is a diagnostic tool used for the assessment of the relationship of various bones and soft tissues in the head and face. Cephalometric analysis has been traditionally conducted with the use of 2D radiographs and landmark sets and restricted to size, linear and angular measurements, and 2D relationships. The increasing use of 3D cone beam computed tomography (CBCT) scans in the dental field dictates the need for the evolution to 3D cephalometric analysis, which incorporates shape and a more realistic analysis of longitudinal development in all three planes. This study is a demonstration of 3D cephalometric analysis with the use of a validated set of skeletal tissue landmarks on human CBCT scans. Detailed instructions for the annotation of each landmark on a 3D volume are provided as part of a step-by-step protocol. The generated measurements and 3D coordinates of the landmarks can be exported and used both for clinical and research purposes. The introduction of 3D cephalometric analysis in basic and clinical craniofacial studies will lead to future advancements in the field of craniofacial growth and development.

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans  

Presented here is a detailed protocol for the conduction of three-dimensional cephalometric analysis with the use of human cone beam computed tomography scans.

Craniofacial cephalometric analysis is a diagnostic tool used for the assessment of the relationship of various bones and soft tissues in the head and ...

Enhancing Pulmonary Nodule Diagnosis Using Whole-Lung 3D Reconstruction

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the entire lung. This is crucial for identifying the primary lesion and developing more scientifically grounded treatment plans for doctors. However, pattern recognition methods based on machine vision are susceptible to false positives and false negatives and, therefore, cannot fully meet clinical demands in this regard. Visualization methods based on maximum intensity projection (MIP) can better illustrate local and individual pulmonary nodules but lack a macroscopic and holistic description of the distribution and spatial features of multiple pulmonary nodules.
Therefore, this study proposes a whole-lung 3D reconstruction method. It extracts the 3D contour of the lung using medical image processing technology against the background of the entire lung and performs 3D reconstruction of the lung, pulmonary artery, and multiple pulmonary nodules in 3D space. This method can comprehensively depict the spatial distribution and radiological features of multiple nodules throughout the entire lung, providing a simple and convenient means of evaluating the diagnosis and prognosis of multiple pulmonary nodules.

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer 

This study introduces a three-dimensional (3D) reconstruction method for the entire lung in patients with early multiple pulmonary nodules. It offers a comprehensive visualization of nodule distribution and their interplay with lung tissue, simplifying the assessment of diagnosis and prognosis for these patients.

For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, ...

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Summary

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Chapters in this video

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Measuring the Complete-arch Distortion of an Optical Dental Impression

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Analysis of Craniomaxillofacial Malformations in Mice Using Three-dimensional Microcomputed Tomography

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules