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Ben Yedder H, Cardoen B, Shokoufi M, Golnaraghi F, Hamarneh G. Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:515-530. [PMID: 34606449 DOI: 10.1109/tmi.2021.3117276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconstruction complexity. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain an accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Applying our method, we find that we can reconstruct and localize lesions faithfully while allowing real-time reconstruction. We also demonstrate that the present algorithm can reconstruct multiple cancer lesions. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.
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Zhang M, Li S, Zou Y, Zhu Q. Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210118RR. [PMID: 34672146 PMCID: PMC8527162 DOI: 10.1117/1.jbo.26.10.106004] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 08/30/2021] [Indexed: 05/02/2023]
Abstract
SIGNIFICANCE In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue. AIM We aim to reduce the chest wall's effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction. APPROACH We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall. RESULTS The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth. CONCLUSIONS Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties.
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Affiliation(s)
- Menghao Zhang
- Washington University in St. Louis, Department of Electrical and Systems Engineering, St. Louis, Missouri, United States
| | - Shuying Li
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Yun Zou
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Quing Zhu
- Washington University in St. Louis, Department of Electrical and Systems Engineering, St. Louis, Missouri, United States
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
- Address all correspondence to Quing Zhu,
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Ursuleanu TF, Luca AR, Gheorghe L, Grigorovici R, Iancu S, Hlusneac M, Preda C, Grigorovici A. Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images. Diagnostics (Basel) 2021; 11:1373. [PMID: 34441307 PMCID: PMC8393354 DOI: 10.3390/diagnostics11081373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 12/13/2022] Open
Abstract
The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their "key" features, for completion of tasks in current applications in the interpretation of medical images. The use of "key" characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.
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Affiliation(s)
- Tudor Florin Ursuleanu
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Surgery VI, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
- Department of Surgery I, Regional Institute of Oncology, 700483 Iasi, Romania
| | - Andreea Roxana Luca
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department Obstetrics and Gynecology, Integrated Ambulatory of Hospital “Sf. Spiridon”, 700106 Iasi, Romania
| | - Liliana Gheorghe
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Radiology, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
| | - Roxana Grigorovici
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Stefan Iancu
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Maria Hlusneac
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Cristina Preda
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Endocrinology, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
| | - Alexandru Grigorovici
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Surgery VI, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
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Cardoen B, Yedder HB, Sharma A, Chou KC, Nabi IR, Hamarneh G. ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1942-1956. [PMID: 31880546 DOI: 10.1109/tmi.2019.2962361] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the forefront of scientific discovery in cancer, infectious, and degenerative diseases. By stochastic temporal and spatial separation of light emissions from fluorescent labelled proteins, SMLM is capable of nanometer scale reconstruction of cellular structures. Precise localization of proteins in 3D astigmatic SMLM is dependent on parameter sensitive preprocessing steps to select regions of interest. With SMLM acquisition highly variable over time, it is non-trivial to find an optimal static parameter configuration. The high emitter density required for reconstruction of complex protein structures can compromise accuracy and introduce artifacts. To address these problems, we introduce two modular auto-tuning pre-processing methods: adaptive signal detection and learned recurrent signal density estimation that can leverage the information stored in the sequence of frames that compose the SMLM acquisition process. We show empirically that our contributions improve accuracy, precision and recall with respect to the state of the art. Both modules auto-tune their hyper-parameters to reduce the parameter space for practitioners, improve robustness and reproducibility, and are validated on a reference in silico dataset. Adaptive signal detection and density prediction can offer a practitioner, in addition to informed localization, a tool to tune acquisition parameters ensuring improved reconstruction of the underlying protein complex. We illustrate the challenges faced by practitioners in applying SMLM algorithms on real world data markedly different from the data used in development and show how ERGO can be run on new datasets without retraining while motivating the need for robust transfer learning in SMLM.
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