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1. Polarization-sensitive optical coherence tomography for renal tumor detection in ex vivo human kidneys

   https://doi.org/10.1016/j.optlaseng.2023.107900  

   Yan, Feng; Wang, Chen; Yan, Yuyang; Zhang, Qinghao; Yu, Zhongxin; Patel, Sanjay G.; Fung, Kar-Ming; Tang, Qinggong (February 2024, Optics and Lasers in Engineering)

   Kidney cancer is a kind of high mortality cancer because of the difficulty in early diagnosis and the high metastatic dissemination in treatments. The surgical resection of tumors is the most effective treatment for renal cancer patients. However, precise assessment of tumor margins is a challenge during surgical resection. The objective of this study is to demonstrate an optical imaging tool in precisely distinguishing kidney tumor borders and identifying tumor zones from normal tissues to assist surgeons in accurately resecting tumors from kidneys during the surgery. 30 samples from six human kidneys were imaged using polarization-sensitive optical coherence tomography (PS-OCT). Cross-sectional, enface, and spatial information of kidney samples were obtained for microenvironment reconstruction. Polarization parameters (phase retardation, optic axis direction, and degree of polarization uniformity (DOPU) and Stokes parameters (Q, U, and V) were utilized for multiparameter analysis. To verify the detection accuracy of PS-OCT, H&E histology staining and dice-coefficient were utilized to quantify the performance of PS-OCT in identifying tumor borders and regions. In this study, tumor borders were clearly identified by PS-OCT imaging, which outperformed the conventional intensity-based OCT. With H&E histological staining as golden standard, PS-OCT precisely identified the tumor regions and tissue distributions at different locations and different depths based on polarization and Stokes parameters. Compared to the traditional attenuation coefficient quantification method, PS-OCT demonstrated enhanced contrast of tissue characteristics between normal and cancerous tissues due to the birefringence effects. Our results demonstrated that PS-OCT was promising to provide imaging guidance for the surgical resection of kidney tumors and had the potential to be used for other human kidney surgeries in clinics such as renal biopsy. 

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2. Enhancing epidural needle guidance using a polarization‐sensitive optical coherence tomography probe with convolutional neural networks

   https://doi.org/10.1002/jbio.202300330  

   Wang, Chen; Liu, Yunlong; Calle, Paul; Li, Xinwei; Liu, Ronghao; Zhang, Qinghao; Yan, Feng; Fung, Kar‐ming; Conner, Andrew K.; Chen, Sixia; et al (October 2023, Journal of Biophotonics)

   Abstract Epidural anesthesia helps manage pain during different surgeries. Nonetheless, the precise placement of the epidural needle remains a challenge. In this study, we developed a probe based on polarization‐sensitive optical coherence tomography (PS‐OCT) to enhance the epidural anesthesia needle placement. The probe was tested on six porcine spinal samples. The multimodal imaging guidance used the OCT intensity mode and three distinct PS‐OCT modes: (1) phase retardation, (2) optic axis, and (3) degree of polarization uniformity (DOPU). Each mode enabled the classification of different epidural tissues through distinct imaging characteristics. To further streamline the tissue recognition procedure, convolutional neural network (CNN) were used to autonomously identify the tissue types within the probe's field of view. ResNet50 models were developed for all four imaging modes. DOPU imaging was found to provide the highest cross‐testing accuracy of 91.53%. These results showed the improved precision by PS‐OCT in guiding epidural anesthesia needle placement. 

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3. Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography

   https://doi.org/10.1109/ACCESS.2025.3595838  

   Cui, Haoyang; Wang, Chen; Calle, Paul; Liu, Yunlong; Zhang, Qinghao; Ly, Sinaro; Reynolds, Justin; Yan, Feng; Zhang, Ke; Liu, Ronghao; et al (August 2025, IEEE Access)

   Optical coherence tomography (OCT) imaging enables high resolution visualization of sub-surface tissue microstructures. However, OCT image analysis using deep learning is hampered by limited diverse training data to meet performance requirements and high inference latency for real-time applications. To address these challenges, we developed Octascope, a lightweight domain-specific convolutional neural network (CNN) - based model designed for OCT image analysis. Octascope was pre-trained using a curriculum learning approach, which involves sequential training, first on natural images (ImageNet), then on OCT images from retinal, abdominal, and renal tissues, to progressively acquire transferable knowledge. This multi-domain pre-training enables Octascope to generalize across varied tissue types. In two downstream tasks, Octascope demonstrated notable improvements in predictive accuracy compared to alternative approaches. In the epidural tissue detection task, our method surpassed single-task learning with fine-tuning by 9.13% and OCT-specific transfer learning by 5.95% in accuracy. Octascope outperformed VGG16 and ResNet50 by 5.36% and 6.66% in a retinal diagnosis task, respectively. In comparison to a Transformer-based OCT foundation model - RETFound, Octascope delivered 2 to 4.4 times faster inference speed with slightly better predictive accuracies in both downstream tasks. Octascope represented a significant advancement for OCT image analysis by providing an effective balance between computational efficiency and diagnostic accuracy for real-time clinical applications. 

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4. Collaborative Suturing: A Reinforcement Learning Approach to Automate Hand-off Task in Suturing for Surgical Robots

   https://doi.org/10.1109/RO-MAN47096.2020.9223543  

   Varier, Vignesh Manoj; Rajamani, Dhruv Kool; Goldfarb, Nathaniel; Tavakkolmoghaddam, Farid; Munawar, Adnan; Fischer, Gregory S (August 2020, 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN))

   null (Ed.)

   Over the past decade, Robot-Assisted Surgeries (RAS), have become more prevalent in facilitating successful operations. Of the various types of RAS, the domain of collaborative surgery has gained traction in medical research. Prominent examples include providing haptic feedback to sense tissue consistency, and automating sub-tasks during surgery such as cutting or needle hand-off - pulling and reorienting the needle after insertion during suturing. By fragmenting suturing into automated and manual tasks the surgeon could essentially control the process with one hand and also circumvent workspace restrictions imposed by the control interface present at the surgeon's side during the operation. This paper presents an exploration of a discrete reinforcement learning-based approach to automate the needle hand-off task. Users were asked to perform a simple running suture using the da Vinci Research Kit. The user trajectory was learnt by generating a sparse reward function and deriving an optimal policy using Q-learning. Trajectories obtained from three learnt policies were compared to the user defined trajectory. The results showed a root-mean-square error of [0.0044mm, 0.0027mm, 0.0020mm] in ℝ 3 . Additional trajectories from varying initial positions were produced from a single policy to simulate repeated passes of the hand-off task. 

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5. Deep learning-based transformation of H&E stained tissues into special stains

   https://doi.org/10.1038/s41467-021-25221-2  

   de Haan, Kevin; Zhang, Yijie; Zuckerman, Jonathan E.; Liu, Tairan; Sisk, Anthony E.; Diaz, Miguel F. P.; Jen, Kuang-Yu; Nobori, Alexander; Liou, Sofia; Zhang, Sarah; et al (August 2021, Nature Communications)

   Abstract Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson’s Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost. 

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