Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis
IEEE TRANSACTIONS ON MEDICAL IMAGING
2020; 39 (12): 4023–33
The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making. However, developing such automatic solutions is challenging due to the requirement of a large amount of human-annotated data. Recently, unsupervised/self-supervised feature learning techniques receive a lot of attention, as they do not need massive annotations. Most of the current self-supervised methods are analyzed with single imaging modality and there is no method currently utilize multi-modal images for better results. Considering that the diagnostics of various vitreoretinal diseases can greatly benefit from another imaging modality, e.g., FFA, this paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis. To achieve this, we first synthesize the corresponding FFA modality and then formulate a patient feature-based softmax embedding objective. Our objective learns both modality-invariant features and patient-similarity features. Through this mechanism, the neural network captures the semantically shared information across different modalities and the apparent visual similarity between patients. We evaluate our method on two public benchmark datasets for retinal disease diagnosis. The experimental results demonstrate that our method clearly outperforms other self-supervised feature learning methods and is comparable to the supervised baseline. Our code is available at GitHub.
View details for DOI 10.1109/TMI.2020.3008871
View details for Web of Science ID 000595547500024
View details for PubMedID 32746140
A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data.
Nature biomedical engineering
Dimensionality reduction is widely used in the visualization, compression, exploration and classification of data. Yet a generally applicable solution remains unavailable. Here, we report an accurate and broadly applicable data-driven algorithm for dimensionality reduction. The algorithm, which we named 'feature-augmented embedding machine' (FEM), first learns the structure of the data and the inherent characteristics of the data components (such as central tendency and dispersion), denoises the data, increases the separation of the components, and then projects the data onto a lower number of dimensions. We show that the technique is effective at revealing the underlying dominant trends in datasets of protein expression and single-cell RNA sequencing, computed tomography, electroencephalography and wearable physiological sensors.
View details for DOI 10.1038/s41551-020-00635-3
View details for PubMedID 33139824
Estimation of Vascular Permeability in Irregularly Shaped Cancers Using Ultrasound Poroelastography
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2020; 67 (4): 1083–96
Vascular permeability (VP) is a mechanical parameter which plays an important role in cancer initiation, metastasis, and progression. To date, there are only a few non-invasive methods that can be used to image VP in solid tumors. Most of these methods require the use of contrast agents and are expensive, limiting widespread use.In this paper, we propose a new method to image VP in tumors, which is based on the use of ultrasound poroelastography. Estimation of VP by poroelastography requires knowledge of the Young's modulus (YM), Poisson's ratio (PR), and strain time constant (TC) in the tumors. In our method, we find the ellipse which best fits the tumor (regardless of its shape) using an eigen-system-based fitting technique and estimate the YM and PR using Eshelby's elliptic inclusion formulation. A Fourier method is used to estimate the axial strain TC, which does not require any initial guess and is highly robust to noise.It is demonstrated that the proposed method can estimate VP in irregularly shaped tumors with an accuracy of above [Formula: see text] using ultrasound simulation data with signal-to-noise ratio of 20 dB or higher. In vivo feasibility of the proposed technique is demonstrated in an orthotopic mouse model of breast cancer.The proposed imaging method can provide accurate and localized estimation of VP in cancers non-invasively and cost-effectively.Accurate and non-invasive assessment of VP can have a significant impact on diagnosis, prognosis, and treatment of cancers.
View details for DOI 10.1109/TBME.2019.2929134
View details for Web of Science ID 000522351200015
View details for PubMedID 31331877
Non-invasive imaging of Young's modulus and Poisson's ratio in cancers in vivo.
2020; 10 (1): 7266
Alterations of Young's modulus (YM) and Poisson's ratio (PR) in biological tissues are often early indicators of the onset of pathological conditions. Knowledge of these parameters has been proven to be of great clinical significance for the diagnosis, prognosis and treatment of cancers. Currently, however, there are no non-invasive modalities that can be used to image and quantify these parameters in vivo without assuming incompressibility of the tissue, an assumption that is rarely justified in human tissues. In this paper, we developed a new method to simultaneously reconstruct YM and PR of a tumor and of its surrounding tissues based on the assumptions of axisymmetry and ellipsoidal-shape inclusion. This new, non-invasive method allows the generation of high spatial resolution YM and PR maps from axial and lateral strain data obtained via ultrasound elastography. The method was validated using finite element (FE) simulations and controlled experiments performed on phantoms with known mechanical properties. The clinical feasibility of the developed method was demonstrated in an orthotopic mouse model of breast cancer. Our results demonstrate that the proposed technique can estimate the YM and PR of spherical inclusions with accuracy higher than 99% and with accuracy higher than 90% in inclusions of different geometries and under various clinically relevant boundary conditions.
View details for DOI 10.1038/s41598-020-64162-6
View details for PubMedID 32350327
A Robust Method to Estimate the Time Constant of Elastographic Parameters
IEEE TRANSACTIONS ON MEDICAL IMAGING
2019; 38 (6): 1358–70
Novel viscoelastic and poroelastic elastography techniques rely on the accurate estimation of the temporal behavior of the axial or lateral strains and related parameters. From the temporal curve of the elastographic parameter of interest, the time constant (TC) is estimated using analytical models and curve-fitting techniques such as Levenberg-Marquardt (LM), Nelder-Mead (NM), and trust-region reflective (TR). In this paper, we propose a new technique named variable projection (VP) to estimate accurately and robustly the TC and steady-state value of the elastographic parameter of interest from its temporal curve. As a testing platform, the method is used with a novel analytical model, which can be used for both poroelastic and viscoelastic tissues and in most practical experimental conditions of clinical interest. Finite element and ultrasound simulations and experimental results demonstrate that VP is robust to noise and capable of estimating the TC of the elastographic parameter with accuracy higher than that of typically employed curve-fitting techniques. The results also demonstrate that the performance of VP is not affected by an incorrect initial TC guess. For example, in simulations, VP can estimate the TC of axial strain and effective Poisson's ratio accurately for initial guesses ranging from 0.001 to infinite times of the true TC value even in fairly noisy conditions (30-dB signal to noise ratio). In experiments, VP always estimates the axial strain TC reliably, whereas the LM, NM, and TR methods fail to converge or converge to wrong solutions in most of the cases.
View details for DOI 10.1109/TMI.2019.2894782
View details for Web of Science ID 000470829000005
View details for PubMedID 30703014
An analytical poroelastic model of a spherical tumor embedded in normal tissue under creep compression
JOURNAL OF BIOMECHANICS
2019; 89: 48–56
An analytical model for a spherical poroelastic tumor embedded in normal poroelastic tissues under creep compression is presented in this paper. The tissue is modeled as a cylindrical sample containing a spherical inclusion having different material properties. Analytical expression for the volumetric strain generated inside the inclusion during creep compression is obtained. Error analysis is carried out by comparing the results from the developed analytical model with corresponding results obtained from an established finite element software for a number of samples with different material properties. The error is found to be below 2.5% for the samples with a small inclusion and 7% in the samples with a large inclusion. The analytical solutions reported in this paper can greatly impact elasticity imaging techniques aiming at reconstructing mechanical properties of tumors such as Young's modulus, Poisson's ratio, interstitial permeability and vascular permeability.
View details for DOI 10.1016/j.jbiomech.2019.04.009
View details for Web of Science ID 000469156200007
View details for PubMedID 31000348
Non-Invasive Imaging of Normalized Solid Stress in Cancers in Vivo
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM
2019; 7: 4300209
The solid stress (SSg) that develops inside a cancer is an important marker of cancer's growth, invasion and metastasis. Currently, there are no non-invasive methods to image SSg inside tumors. In this paper, we develop a new, non-invasive and cost-effective imaging method to assess SSg inside tumors that uses ultrasound poroelastography. Center to the proposed method is a novel analytical model, which demonstrates that SSg and the compression-induced stress (SSc) that generates inside the cancer in a poroelastography experiment have the same spatial distribution. To show the clinical feasibility of the proposed technique, we imaged and analyzed the normalized SSg inside treated and untreated human breast cancers in a small animal model. Given the clinical significance of assessing SSg in cancers and the advantages of the proposed ultrasonic methods, our technique could have a great impact on cancer diagnosis, prognosis and treatment methods.
View details for DOI 10.1109/JTEHM.2019.2932059
View details for Web of Science ID 000494830100001
View details for PubMedID 32309062
View details for PubMedCentralID PMC6822636
- A New Poroelastography Method to Assess the Solid Distribution in Cancers IEEE ACCESS 2019; 7: 103404–15
A Model-Based Approach to Investigate the Effect of a Long Bone Fracture on Ultrasound Strain Elastography
IEEE TRANSACTIONS ON MEDICAL IMAGING
2018; 37 (12): 2704–17
The mechanical behavior of long bones and fractures has been under investigation for many decades due to its complexity and clinical relevance. In this paper, we report a new subject-specific methodology to predict and analyze the mechanical behavior of the soft tissue at a bone interface with the intent of identifying the presence and location of bone abnormalities with high accuracy, spatial resolution, and contrast. The proposed methodology was tested on both intact and fractured rabbit femur samples with finite element-based 3-D simulations, created from actual femur computed tomography data, and ultrasound elastography experiments. The results included in this study demonstrate that elastographic strains at the bone/soft tissue interface can be used to differentiate fractured femurs from the intact ones on a distribution level. These results also demonstrate that coronal plane axial shear strain creates a unique contrast mechanism that can be used to reliably detect fractures (both complete and incomplete) in long bones. Kruskal-Wallis test further demonstrates that the contrast measure for the fracture group (simulation: 2.1286±0.2206; experiment: 2.7034 ± 1.0672) is significantly different from that for the intact group (simulation: 0 ± 0; experiment: 1.1540±0.6909) when using coronal plane axial shear strain elastography ( < 0.01). We conclude that: 1) elastography techniques can be used to accurately identify the presence and location of fractures in a long bone and 2) the proposed model-based approach can be used to predict and analyze strains at a bone fracture site and to better interpret experimental elastographic data.
View details for DOI 10.1109/TMI.2018.2849996
View details for Web of Science ID 000451903400015
View details for PubMedID 29994472
A New Method for Estimating the Effective Poisson's Ratio in Ultrasound Poroelastography
IEEE TRANSACTIONS ON MEDICAL IMAGING
2018; 37 (5): 1178–91
Ultrasound poroelastography aims at assessing the poroelastic behavior of biological tissues via estimation of the local temporal axial strains and effective Poisson's ratios (EPR). Currently, reliable estimation of EPR using ultrasound is a challenging task due to the limited quality of lateral strain estimation. In this paper, we propose a new two-step EPR estimation technique based on dynamic programming elastography (DPE) and Horn-Schunck (HS) optical flow estimation. In the proposed method, DPE is used to estimate the integer axial and lateral displacements while HS is used to obtain subsample axial and lateral displacements from the motion-compensated pre-compressed and post-compressed radio frequency data. Axial and lateral strains are then calculated using Kalman filter-based least square estimation. The proposed two-step technique was tested using finite-element simulations, controlled experiments and in vivo experiments, and its performance was statistically compared with that of analytic minimization (AM) and correlation-based method (CM). Our results indicate that our technique provides EPR elastograms of higher quality and accuracy than those produced by AM and CM. Regarding signal-to-noise ratio and elastographic contrast-to-noise ratio, in simulated data, the proposed method provides an average improvement of 30% and 75%, respectively, with respect to AM and of 100% and 169%, respectively, with respect to CM, whereas, in experiments, the proposed approach provides an average improvement of 30% and 67% with respect to AM and of 230% and 525% with respect to CM. Based on these results, the proposed method may be the preferred one in experimental poroelastography applications.
View details for DOI 10.1109/TMI.2018.2792437
View details for Web of Science ID 000431544500010
View details for PubMedID 29727281
An analytical poroelastic model for ultrasound elastography imaging of tumors
PHYSICS IN MEDICINE AND BIOLOGY
2018; 63 (2): 025031
The mechanical behavior of biological tissues has been studied using a number of mechanical models. Due to the relatively high fluid content and mobility, many biological tissues have been modeled as poroelastic materials. Diseases such as cancers are known to alter the poroelastic response of a tissue. Tissue poroelastic properties such as compressibility, interstitial permeability and fluid pressure also play a key role for the assessment of cancer treatments and for improved therapies. At the present time, however, a limited number of poroelastic models for soft tissues are retrievable in the literature, and the ones available are not directly applicable to tumors as they typically refer to uniform tissues. In this paper, we report the analytical poroelastic model for a non-uniform tissue under stress relaxation. Displacement, strain and fluid pressure fields in a cylindrical poroelastic sample containing a cylindrical inclusion during stress relaxation are computed. Finite element simulations are then used to validate the proposed theoretical model. Statistical analysis demonstrates that the proposed analytical model matches the finite element results with less than 0.5% error. The availability of the analytical model and solutions presented in this paper may be useful to estimate diagnostically relevant poroelastic parameters such as interstitial permeability and fluid pressure, and, in general, for a better interpretation of clinically-relevant ultrasound elastography results.
View details for DOI 10.1088/1361-6560/aa9631
View details for Web of Science ID 000422867100009
View details for PubMedID 29336354