Femur segmentation in DXA imaging using a machine learning decision tree

Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations

BACKGROUND

The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking.

OBJECTIVE

This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress.

METHODS

Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness.

RESULTS

Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT.

FUTURE DIRECTIONS

The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain.

CONCLUSION

Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.

Automatic Femoral Deformity Analysis Based on the Constrained Local Models and Hough Forest

Clinically, Taylor spatial frame (TSF) is usually used to correct femoral deformity. The first step in correction is to analyze skeletal deformities and measure the center of rotation of angulation (CORA). Since the above work needs to be done manually, the doctor's workload is heavy. Therefore, an automatic femoral deformity analysis system was proposed. Firstly, the Hough forest and constrained local models were trained on the femur image set. Then, the position and size of the femur in the X-ray image were detected by the trained Hough forest. Furthermore, the position and size were served as the initial values of the trained constrained local models to fit the femoral contour. Finally, the anatomical axis line of the proximal femur and the anatomical axis line of the distal femur could be drawn according to the fitting results. According to these lines, CORA can be found. Compared with manual measurement by doctors, the average error of the hip joint orientation line was 1.7°, the standard deviation was 1.75, the average error of the anatomic axis line of the proximal femur was 2.9°, and the standard deviation was 3.57. The automatic femoral deformity analysis system meets the accuracy requirements of orthopedics and can significantly reduce the workload of doctors.

Additive manufacturing pertaining to bone: Hopes, reality and future challenges for clinical applications

For the past 20 years, the democratization of additive manufacturing (AM) technologies has made many of us dream of: low cost, waste-free, and on-demand production of functional parts; fully customized tools; designs limited by imagination only, etc. As every patient is unique, the potential of AM for the medical field is thought to be considerable: AM would allow the division of dedicated patient-specific healthcare solutions entirely adapted to the patients' clinical needs. Pertinently, this review offers an extensive overview of bone-related clinical applications of AM and ongoing research trends, from 3D anatomical models for patient and student education to ephemeral structures supporting and promoting bone regeneration. Today, AM has undoubtably improved patient care and should facilitate many more improvements in the near future. However, despite extensive research, AM-based strategies for bone regeneration remain the only bone-related field without compelling clinical proof of concept to date. This may be due to a lack of understanding of the biological mechanisms guiding and promoting bone formation and due to the traditional top-down strategies devised to solve clinical issues. Indeed, the integrated holistic approach recommended for the design of regenerative systems (i.e., fixation systems and scaffolds) has remained at the conceptual state. Challenged by these issues, a slower but incremental research dynamic has occurred for the last few years, and recent progress suggests notable improvement in the years to come, with in view the development of safe, robust and standardized patient-specific clinical solutions for the regeneration of large bone defects.

Artificial intelligence, osteoporosis and fragility fractures

PURPOSE OF REVIEW

Artificial intelligence tools have found new applications in medical diagnosis. These tools have the potential to capture underlying trends and patterns, otherwise impossible with previous modeling capabilities. Machine learning and deep learning models have found a role in osteoporosis, both to model the risk of fragility fracture, and to help with the identification and segmentation of images.

RECENT FINDINGS

Here we survey the latest research in the artificial intelligence application to the prediction of osteoporosis that has been published between January 2017 and March 2019. Around half of the articles that are covered here predict (by classification or regression) an indicator of osteoporosis, such as bone mass or fragility fractures; the other half of studies use tools for automatic segmentation of the images of patients with or at risk of osteoporosis. The data for these studies include diverse signal sources: acoustics, MRI, CT, and of course, X-rays.

SUMMARY

New methods for automatic image segmentation, and prediction of fracture risk show promising clinical value. Though these recent developments have had a successful initial application to osteoporosis research, their development is still under improvement, such as accounting for positive/negative class bias. We urge care when reporting accuracy metrics, and when comparing such metrics between different studies.

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

Artificial intelligence is upending many of our assumptions about the ability of computers to detect and diagnose diseases on medical images. Deep learning, a recent innovation in artificial intelligence, has shown the ability to interpret medical images with sensitivities and specificities at or near that of skilled clinicians for some applications. In this review, we summarize the history of artificial intelligence, present some recent research advances, and speculate about the potential revolutionary clinical impact of the latest computer techniques for bone and muscle imaging. © 2019 American Society for Bone and Mineral Research. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

Diagnosis of Osteoporosis using modified U-net architecture with attention unit in DEXA and X-ray images

BACKGROUND

Osteoporosis, a silent killing disease of fracture risk, is normally determined based on the bone mineral density (BMD) and T-score values measured in bone. However, development of standard algorithms for accurate segmentation and BMD measurement from X-ray images is a challenge in the medical field.

OBJECTIVE

The purpose of this work is to more accurately measure BMD from X-ray images, which can overcome the limitations of the current standard technique to measure BMD using Dual Energy X-ray Absorptiometry (DEXA) such as non-availability and inaccessibility of DEXA machines in developing countries. In addition, this work also attempts to analyze the DEXA scan images for better segmentation and measurement of BMD.

METHODS

This work employs a modified U-Net with Attention unit for accurate segmentation of bone region from X-Ray and DEXA images. A linear regression model is developed to compute BMD and T-score. Based on the value of T-score, the images are then classified as normal, osteopenia or osteoporosis.

RESULTS

The proposed network is experimented with the two internally collected datasets namely, DEXSIT and XSITRAY, comprised of DEXA and X-ray images, respectively. The proposed method achieved an accuracy of 88% on both datasets. The Dice score on DEXSIT and XSITRAY is 0.94 and 0.92, respectively.

CONCLUSION

Our modified U-Net with attention unit achieves significantly higher results in terms of Dice score and classification accuracy. The computed BMD and T-score values of the proposed method are also compared with the respective clinical reports for validation. Hence, using the digitized X-Ray images can be used to detect osteoporosis efficiently and accurately.

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

OBJECTIVES

This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to "classical" knowledge-based ones, and to consider the issues raised and their possible solutions.

METHODS

We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies.

RESULTS

We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data.

CONCLUSIONS

Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

Computer-aided osteoporosis detection from DXA imaging

BACKGROUND AND OBJECTIVE

Osteoporosis is a skeletal disease caused by a high rate of bone tissue loss, and it is a major cause of bone fracture. In contemporary society, osteoporosis is more common than cancer and stroke and results in a higher rate of morbidity and mortality in the human population. Osteoporosis can conclusively be diagnosed with dual energy X-ray absorptiometry (DXA). In this study, we propose a computer-aided osteoporosis detection (CAOD) technique that automatically measures bone mineral density (BMD) and generates an osteoporosis report from a DXA scan.

METHODS

The CAOD model denoise and segments DXA images using a non-local mean filter, Machine learning pixel label random forest respectively, and locates regions of interest with higher accuracy. Pixel label random forest classifies a pixel either bone or soft tissue; then contours are extracted from binary image to locate regions of interest and calculate BMD from bone and soft tissues pixels. Mean standard deviation and correlation coefficients statistical analysis were used to evaluate the consistency and accuracy of BMD measurements.

RESULTS

During a consistency test of BMD measurements using three consecutive scans from Computerized Imaging Reference Systems' Bona Fide Phantom (CIRS-BFP) for the spine, the CAOD model showed an averaged standard deviation of 0.0029 while the standard deviation from manual measurements on the same data set by three different individuals was recorded as 0.1199. During another correlation study of BMD measurements evaluating real human scan images by the CAOD model versus manual measurement, the model scored a correlation coefficient of R² = 0.9901 while the CIRS-BFP study scored a correlation coefficient of R² = 0.9709.

CONCLUSIONS

The CAOD model increases the preciseness and accuracy of BMD measurements. This CAOD method will help clinicians, untrained DXA operators, and researchers (medical scientists, doctors, and bone researchers) use the DXA system with reliable accuracy and overcome workload challenges. It will also improve osteoporosis diagnosis from DXA systems and increase system performance and value.

Automatic hip geometric feature extraction in DXA imaging using regional random forest

BACKGROUND

Hip fracture is considered one of the salient disability factors across the global population. People with hip fractures are prone to become permanently disabled or die from complications. Although currently the premier determiner, bone mineral density has some notable limitations in terms of hip fracture risk assessment.

OBJECTIVES

To learn more about bone strength, hip geometric features (HGFs) can be collected. However, organizing a hip fracture risk study for a large population using a manual HGFs collection technique would be too arduous to be practical. Thus, an automatic HGFs extraction technique is needed.

METHOD

This paper presents an automated HGFs extraction technique using regional random forest. Regional random forest localizes landmark points from femur DXA images using local constraints of hip anatomy. The local region constraints make random forest robust to noise and increase its performance because it processes the least number of points and patches.

RESULTS

The proposed system achieved an overall accuracy of 96.22% and 95.87% on phantom data and real human scanned data respectively.

CONCLUSION

The proposed technique's ability to measure HGFs could be useful in research on the cause and facts of hip fracture and could help in the development of new guidelines for hip fracture risk assessment in the future. The technique will reduce workload and improve the use of X-ray devices.