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Your search keyword '"Cha, Jaepyeong"' showing total 48 results
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48 results on '"Cha, Jaepyeong"'

1. DXM-TransFuse U-net: Dual Cross-Modal Transformer Fusion U-net for Automated Nerve Identification

Author

Xie, Baijun, Milam, Gary, Ning, Bo, Cha, Jaepyeong, and Park, Chung Hyuk

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate nerve identification is critical during surgical procedures for preventing any damages to nerve tissues. Nerve injuries can lead to long-term detrimental effects for patients as well as financial overburdens. In this study, we develop a deep-learning network framework using the U-Net architecture with a Transformer block based fusion module at the bottleneck to identify nerve tissues from a multi-modal optical imaging system. By leveraging and extracting the feature maps of each modality independently and using each modalities information for cross-modal interactions, we aim to provide a solution that would further increase the effectiveness of the imaging systems for enabling the noninvasive intraoperative nerve identification.

Published
2022
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7. Emerging Imaging Technologies for Parathyroid Gland Identification and Vascular Assessment in Thyroid Surgery

Author

Silver Karcioglu, Amanda L., primary, Triponez, Frédéric, additional, Solórzano, Carmen C., additional, Iwata, Ayaka J., additional, Abdelhamid Ahmed, Amr H., additional, Almquist, Martin, additional, Angelos, Peter, additional, Benmiloud, Fares, additional, Berber, Eren, additional, Bergenfelz, Anders, additional, Cha, Jaepyeong, additional, Colaianni, C. Alessandra, additional, Davies, Louise, additional, Duh, Quan-Yang, additional, Hartl, Dana, additional, Kandil, Emad, additional, Kim, Wan Wook, additional, Kopp, Peter A., additional, Liddy, Whitney, additional, Mahadevan-Jansen, Anita, additional, Lee, Kang-Dae, additional, Mannstadt, Michael, additional, McMullen, Caitlin P., additional, Shonka, David C., additional, Shin, Jennifer J., additional, Singer, Michael C., additional, Slough, Cristian M., additional, Stack, Brendan C., additional, Tearney, Guillermo, additional, Thomas, Giju, additional, Tolley, Neil, additional, Vidal-Fortuny, Jordi, additional, and Randolph, Gregory W., additional

Published
2023
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11. A coaxial excitation, dual‐red‐green‐blue/near‐infrared paired imaging system toward computer‐aided detection of parathyroid glands in situ and ex vivo

Author

Kim, Yoseph, primary, Lee, Hun Chan, additional, Kim, Jongchan, additional, Oh, Eugene, additional, Yoo, Jennifer, additional, Ning, Bo, additional, Lee, Seung Yup, additional, Ali, Khalid Mohamed, additional, Tufano, Ralph P., additional, Russell, Jonathon O., additional, and Cha, Jaepyeong, additional

Published
2022
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31. Widefield illumination photoacoustic imaging of blood vessel phantom.

Author

Han, Jae‐Ho and Cha, Jaepyeong

Subjects
*ACOUSTIC imaging, *BLOOD vessels, *TOMOGRAPHY, *PULSED lasers, *IMAGE reconstruction, *PHOTOACOUSTIC spectroscopy, *PHOTOACOUSTIC effect
Abstract

We achieved photoacoustic tomographic imaging by employing widefield illumination using a pulsed laser source that irradiates the entire area of interest for the specimen under investigation. This approach can be simpler and more efficient than the use of narrow point‐source scanning. Tomographic images were computed via an inverse‐reconstruction‐based algorithm from signals acquired using a single transducer with circular scanning configuration. The signals were coupled to the transducer probe with index‐matching gel around the circumference of the specimen, eliminating the need for aquatic immersion. A blood‐vessel‐mimicking phantom was used to demonstrate our setup and the image reconstruction was also verified by simulation, proving the feasibility of a miniaturized system based on this design. [ABSTRACT FROM AUTHOR]

Published
2020
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45. Multicore fiber optic imaging reveals that astrocyte calcium activity in the cerebral cortex is modulated by internal motivational state.

Author

Gau YA, Hsu E, Cha J, Pak RW, Looger LL, Kang JU, and Bergles DE

Abstract

Astrocytes are a direct target of neuromodulators and can influence neuronal activity on broad spatial and temporal scales through their close proximity to synapses. However, our knowledge about how astrocytes are functionally recruited during different animal behaviors and their diverse effects on the CNS remains limited. To enable measurement of astrocyte activity patterns in vivo during normative behaviors, we developed a high-resolution, long working distance, multi-core fiber optic imaging platform that allows visualization of cortical astrocyte calcium transients through a cranial window in freely moving mice. Using this platform, we defined the spatiotemporal dynamics of astrocytes during diverse behaviors, ranging from circadian fluctuations to novelty exploration, showing that astrocyte activity patterns are more variable and less synchronous than apparent in head-immobilized imaging conditions. Although the activity of astrocytes in visual cortex was highly synchronized during quiescence to arousal transitions, individual astrocytes often exhibited distinct thresholds and activity patterns during explorative behaviors, in accordance with their molecular diversity, allowing temporal sequencing across the astrocyte network. Imaging astrocyte activity during self-initiated behaviors revealed that noradrenergic and cholinergic systems act synergistically to recruit astrocytes during state transitions associated with arousal and attention, which was profoundly modulated by internal state. The distinct activity patterns exhibited by astrocytes in the cerebral cortex may provide a means to vary their neuromodulatory influence in response to different behaviors and internal states., Competing Interests: Declaration of Interests The authors declare that there is no conflict of interest.

Published
2023
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46. Emerging Imaging Technologies for Parathyroid Gland Identification and Vascular Assessment in Thyroid Surgery: A Review From the American Head and Neck Society Endocrine Surgery Section.

Author

Silver Karcioglu AL, Triponez F, Solórzano CC, Iwata AJ, Abdelhamid Ahmed AH, Almquist M, Angelos P, Benmiloud F, Berber E, Bergenfelz A, Cha J, Colaianni CA, Davies L, Duh QY, Hartl D, Kandil E, Kim WW, Kopp PA, Liddy W, Mahadevan-Jansen A, Lee KD, Mannstadt M, McMullen CP, Shonka DC Jr, Shin JJ, Singer MC, Slough CM, Stack BC Jr, Tearney G, Thomas G, Tolley N, Vidal-Fortuny J, and Randolph GW

Subjects
Humans, Thyroid Gland diagnostic imaging, Thyroid Gland surgery, Indocyanine Green, Optical Imaging adverse effects, Optical Imaging methods, Thyroidectomy methods, Parathyroid Glands diagnostic imaging, Parathyroid Glands surgery, Hypoparathyroidism etiology
Abstract

Importance: Identification and preservation of parathyroid glands (PGs) remain challenging despite advances in surgical techniques. Considerable morbidity and even mortality result from hypoparathyroidism caused by devascularization or inadvertent removal of PGs. Emerging imaging technologies hold promise to improve identification and preservation of PGs during thyroid surgery., Observation: This narrative review (1) comprehensively reviews PG identification and vascular assessment using near-infrared autofluorescence (NIRAF)-both label free and in combination with indocyanine green-based on a comprehensive literature review and (2) offers a manual for possible implementation these emerging technologies in thyroid surgery., Conclusions and Relevance: Emerging technologies hold promise to improve PG identification and preservation during thyroidectomy. Future research should address variables affecting the degree of fluorescence in NIRAF, standardization of signal quantification, definitions and standardization of parameters of indocyanine green injection that correlate with postoperative PG function, the financial effect of these emerging technologies on near-term and longer-term costs, the adoption learning curve and effect on surgical training, and long-term outcomes of key quality metrics in adequately powered randomized clinical trials evaluating PG preservation.

Published
2023
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47. Development of a non-invasive, dual-sensor handheld imager for intraoperative preservation of parathyroid glands.

Author

Oh E, Kim Y, Ning B, Lee SY, Kim WW, and Cha J

Subjects
Diagnostic Imaging, Pilot Projects, Thyroid Gland diagnostic imaging, Thyroid Gland surgery, Parathyroid Glands diagnostic imaging, Parathyroid Glands surgery, Thyroidectomy
Abstract

Intraoperative localization and preservation of parathyroid glands (PTGs) are challenging during thyroid surgery. Using a technique of combined near-infrared PTG autofluorescence detection and dye-free imaging angiography, this study developed a portable device for localization of PTGs and assessment of viability by confirming tissue perfusion. The imager's performance was evaluated through a pilot clinical study (N=10).

Published
2021
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48. Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images.

Author

Ngo L, Cha J, and Han JH

Abstract

Segmenting the retinal layers in optical coherence tomography (OCT) images helps to quantify the layer information in early diagnosis of retinal diseases, which are the main cause of permanent blindness. Thus, the segmentation process plays a critical role in preventing vision impairment. However, because there is a lack of practical automated techniques, expert ophthalmologists still have to manually segment the retinal layers. In this study, we propose an automated segmentation method for OCT images based on a feature-learning regression network without human bias. The proposed deep neural network regression takes the intensity, gradient, and adaptive normalized intensity score (ANIS) of an image segment as features for learning, and then predicts the corresponding retinal boundary pixel. Reformulating the segmentation as a regression problem obviates the need for a huge dataset and reduces the complexity significantly, as shown in the analysis of computational complexity given here. In addition, assisted by ANIS, the method operates robustly on OCT images containing intensity variances, low-contrast regions, speckle noise, and blood vessels, yet remains accurate and time-efficient. In evaluation of the method conducted using 114 images, the processing time was approximately 10.596 s per image for identifying eight boundaries, and the training phase for each boundary line took only 30 s. Further, the Dice similarity coefficient used for assessing accuracy gave a computed value of approximately 0.966. The absolute pixel distance of manual and automatic segmentation using the proposed scheme was 0.612, which is less than a one-pixel difference, on average.

Published
2019
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