Md Tauhidul Islam

All Publications

• Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data. Nature communications Islam, M. T., Xing, L. 2023; 14 (1): 679

  Abstract

  Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.

  View details for DOI 10.1038/s41467-023-36383-6

  View details for PubMedID 36755047

• Leveraging data-driven self-consistency for high-fidelity gene expression recovery. Nature communications Islam, M. T., Wang, J., Ren, H., Li, X., Khuzani, M. B., Sang, S., Yu, L., Shen, L., Zhao, W., Xing, L. 2022; 13 (1): 7142

  Abstract

  Single cell RNA sequencing is a promising technique to determine the states of individual cells and classify novel cell subtypes. In current sequence data analysis, however, genes with low expressions are omitted, which leads to inaccurate gene counts and hinders downstream analysis. Recovering these omitted expression values presents a challenge because of the large size of the data. Here, we introduce a data-driven gene expression recovery framework, referred to as self-consistent expression recovery machine (SERM), to impute the missing expressions. Using a neural network, the technique first learns the underlying data distribution from a subset of the noisy data. It then recovers the overall expression data by imposing a self-consistency on the expression matrix, thus ensuring that the expression levels are similarly distributed in different parts of the matrix. We show that SERM improves the accuracy of gene imputation with orders of magnitude enhancement in computational efficiency in comparison to the state-of-the-art imputation techniques.

  View details for DOI 10.1038/s41467-022-34595-w

  View details for PubMedID 36414658

• Utilizing differential characteristics of high dimensional data as a mechanism for dimensionality reduction PATTERN RECOGNITION LETTERS Xing, S. S., Islam, M. 2022; 157: 1-7

  View details for DOI 10.1016/j.patrec.2022.03.015

  View details for Web of Science ID 000792767300001

• Implicit neural representation for radiation therapy dose distribution. Physics in medicine and biology Vasudevan, V., Shen, L., Huang, C., Chuang, C. F., Islam, M. T., Ren, H., Yang, Y., Dong, P., Xing, L. 2022

  Abstract

  OBJECTIVE: Dose distribution data plays a pivotal role in radiotherapy treatment planning. The data is typically represented using voxel grids, and its size ranges from 10^6--10^8. A concise representation of the treatment plan is of great value in facilitating treatment planning and downstream applications. This work aims to develop an implicit neural representation of 3D dose distribution data.APPROACH: Instead of storing the dose values at each voxel, in the proposed approach, the weights of a multilayer perceptron (MLP) are employed to characterize the dosimetric data for plan representation and subsequent applications. We train a coordinate-based MLP with sinusoidal activations to map the voxel spatial coordinates to the corresponding dose values. We identify the best architecture for a given parameter budget and use that to train a model for each patient. The trained MLP is evaluated at each voxel location to reconstruct the dose distribution. We perform extensive experiments on dose distributions of prostate, spine, and head and neck tumor cases to evaluate the quality of the proposed representation. We also study the change in representation quality by varying model size and activation function.MAIN RESULTS: Using coordinate-based MLPs with sinusoidal activations, we can learn implicit representations that achieve a mean-squared error of 10^{-6} and peak signal-to-noise ratio greater than 50 dB at a target bitrate of ~1 across all the datasets, with a compression ratio of ~32. Our results also show that model sizes with a bitrate of 1--2 achieve optimal accuracy. For smaller bitrates, performance starts to drop significantly.SIGNIFICANCE: The proposed model provides a low-dimensional, implicit, and continuous representation of 3D dose data. In summary, given a dose distribution, we systematically show how to find a compact model to fit the data accurately. This study lays the groundwork for future applications of neural representations of dose data in radiation oncology.

  View details for DOI 10.1088/1361-6560/ac6b10

  View details for PubMedID 35477171

• Estimation of Mechanical and Transport Parameters in Cancers Using Short Time Poroelastography IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE Majumder, S., Islam, M., Righetti, R. 2022; 10

  View details for DOI 10.1109/JTEHM.2022.3198316

  View details for Web of Science ID 000844127700001

• Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy. Computers in biology and medicine Liang, X., Bassenne, M., Hristov, D. H., Islam, M. T., Zhao, W., Jia, M., Zhang, Z., Gensheimer, M., Beadle, B., Le, Q., Xing, L. 1800; 141: 105139

  Abstract

  PURPOSE: To develop a deep unsupervised learning method with control volume (CV) mapping from patient positioning daily CT (dCT) to planning computed tomography (pCT) for precise patient positioning.METHODS: We propose an unsupervised learning framework, which maps CVs from dCT to pCT to automatically generate the couch shifts, including translation and rotation dimensions. The network inputs are dCT, pCT and CV positions in the pCT. The output is the transformation parameter of the dCT used to setup the head and neck cancer (HNC) patients. The network is trained to maximize image similarity between the CV in the pCT and the CV in the dCT. A total of 554 CT scans from 158 HNC patients were used for the evaluation of the proposed model. At different points in time, each patient had many CT scans. Couch shifts are calculated for the testing by averaging the translation and rotation from the CVs. The ground-truth of the shifts come from bone landmarks determined by an experienced radiation oncologist.RESULTS: The system positioning errors of translation and rotation are less than 0.47mm and 0.17°, respectively. The random positioning errors of translation and rotation are less than 1.13mm and 0.29°, respectively. The proposed method enhanced the proportion of cases registered within a preset tolerance (2.0mm/1.0°) from 66.67% to 90.91% as compared to standard registrations.CONCLUSIONS: We proposed a deep unsupervised learning architecture for patient positioning with inclusion of CVs mapping, which weights the CVs regions differently to mitigate any potential adverse influence of image artifacts on the registration. Our experimental results show that the proposed method achieved efficient and effective HNC patient positioning.

  View details for DOI 10.1016/j.compbiomed.2021.105139

  View details for PubMedID 34942395

• Artificial intelligence in image-guided radiotherapy: a review of treatment target localization. Quantitative imaging in medicine and surgery Zhao, W., Shen, L., Islam, M. T., Qin, W., Zhang, Z., Liang, X., Zhang, G., Xu, S., Li, X. 2021; 11 (12): 4881-4894

  Abstract

  Modern conformal beam delivery techniques require image-guidance to ensure the prescribed dose to be delivered as planned. Recent advances in artificial intelligence (AI) have greatly augmented our ability to accurately localize the treatment target while sparing the normal tissues. In this paper, we review the applications of AI-based algorithms in image-guided radiotherapy (IGRT), and discuss the indications of these applications to the future of clinical practice of radiotherapy. The benefits, limitations and some important trends in research and development of the AI-based IGRT techniques are also discussed. AI-based IGRT techniques have the potential to monitor tumor motion, reduce treatment uncertainty and improve treatment precision. Particularly, these techniques also allow more healthy tissue to be spared while keeping tumor coverage the same or even better.

  View details for DOI 10.21037/qims-21-199

  View details for PubMedID 34888196

  View details for PubMedCentralID PMC8611462

• Geometry and statistics-preserving manifold emb e dding for nonlinear dimensionality reduction PATTERN RECOGNITION LETTERS Islam, M., Xing, L. 2021; 151: 155-162

  View details for DOI 10.1016/j.patrec.2021.07.012

  View details for Web of Science ID 000697484500002

• Artificial intelligence in image-guided radiotherapy: a review of treatment target localization QUANTITATIVE IMAGING IN MEDICINE AND SURGERY Zhao, W., Shen, L., Islam, M., Qin, W., Zhang, Z., Liang, X., Zhang, G., Xu, S., Li, X. 2021

  View details for DOI 10.21037/qims-21-199

  View details for Web of Science ID 000685217800001

• Non-Invasive Assessment of the Spatial and Temporal Distributions of Interstitial Fluid Pressure, Fluid Velocity and Fluid Flow in Cancers In Vivo IEEE ACCESS Islam, M., Tang, S., Tasciotti, E., Righetti, R. 2021; 9: 89222-89233

  View details for DOI 10.1109/ACCESS.2021.3089454

  View details for Web of Science ID 000673652200001

• Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis IEEE TRANSACTIONS ON MEDICAL IMAGING Li, X., Jia, M., Islam, M., Yu, L., Xing, L. 2020; 39 (12): 4023–33

  Abstract

  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 Islam, M. T., Xing, L. 2020

  Abstract

  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 Islam, M., Tasciotti, E., Righetti, R. 2020; 67 (4): 1083–96

  Abstract

  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. Scientific reports Islam, M. T., Tang, S. n., Liverani, C. n., Saha, S. n., Tasciotti, E. n., Righetti, R. n. 2020; 10 (1): 7266

  Abstract

  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 Islam, M., Chaudhry, A., Righetti, R. 2019; 38 (6): 1358–70

  Abstract

  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 Islam, M., Righetti, R. 2019; 89: 48–56

  Abstract

  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 Islam, M., Tasciotti, E., Righetti, R. 2019; 7: 4300209

  Abstract

  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 Islam, M., Righetti, R. 2019; 7: 103404–15

  View details for DOI 10.1109/ACCESS.2019.2929021

  View details for Web of Science ID 000481692100004

• A Model-Based Approach to Investigate the Effect of a Long Bone Fracture on Ultrasound Strain Elastography IEEE TRANSACTIONS ON MEDICAL IMAGING Tang, S., Sabonghy, E. P., Chaudhry, A., Shajudeen, P., Islam, M., Kim, N., Cabrera, F. J., Reddy, J. N., Tasciotti, E., Righetti, R. 2018; 37 (12): 2704–17

  Abstract

  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 Islam, M., Chaudhry, A., Tang, S., Tasciotti, E., Righetti, R. 2018; 37 (5): 1178–91

  Abstract

  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 Islam, M., Chaudhry, A., Unnikrishnan, G., Reddy, J. N., Righetti, R. 2018; 63 (2): 025031

  Abstract

  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