Clinically, bi-parametric MRI (bp-MRI), including T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient map, offers essential prior localization of biopsy and focal therapy for suspicious clinically significant prostate cancer (csPCa), and accurate csPCa delineation from bp-MRI is crucial for better outcomes. However, due to the complexity and high variability in appearance, size, shape, and indistinct boundaries, delineating csPCa remains challenging, time-consuming, and heavily relies on the clinician's experience. To address these issues, we propose MT-SAM, a novel framework that enhances SAM with higher-quality feature extraction and a prior-guided automatic prompting strategy. Specifically, we introduce a mamba-transformer network to extract multi-stage multi-modal features from bp-MRI and fuse them into the SAM encoder via cross-mamba modules. Moreover, we propose a prior-guided pyramid-mamba prompting strategy to strengthen the model's attention on the targets. We extensively evaluate our method on both public and private datasets, and the experimental results show that our method achieves up to 5.6-34.1% higher Dice scores than state-of-the-art methods. Code is available at https://github.com/LuckLT/MT-SAM.

Automatic laparoscope motion control is fundamentally important for surgeons to efficiently perform operations. However, its traditional control methods based on tool tracking without considering information hidden in surgical scenes are not intelligent enough, while the latest supervised imitation learning (IL)-based methods require expensive sensor data and suffer from distribution mismatch issues caused by limited demonstrations. In this paper, we propose a novel Imitation Learning framework for Laparoscope Control (ILLC) with reinforcement learning (RL), which can efficiently learn the control policy from limited surgical video clips. Specially, we first extract surgical laparoscope trajectories from unlabeled videos as the demonstrations and reconstruct the corresponding surgical scenes. To fully learn from limited motion trajectory demonstrations, we propose Shape Preserving Trajectory Augmentation (SPTA) to augment these data, and build a simulation environment that supports parallel RGB-D rendering to reinforce the RL policy for interacting with the environment efficiently. With adversarial training for IL, we obtain the laparoscope control policy based on the generated rollouts and surgical demonstrations. Extensive experiments are conducted in unseen reconstructed surgical scenes, and our method outperforms the previous IL methods, which proves the feasibility of our unified learning-based framework for laparoscope control.

Since 2017, the TrueNTH Global Registry (TNGR) has aimed to drive improvement in patient outcomes for individuals with localized prostate cancer by collating data from healthcare institutions across 13 countries. As TNGR matures, a systematic evaluation of existing processes and documents is necessary to evaluate whether the registry is operating as intended. The main supporting documents: protocol and data dictionary, were comprehensively reviewed in a series of meetings over a 10-month period by an international working group. In parallel, individual consultations with local institutions regarding a benchmarking quality-of-care report were conducted. Four consensus areas for improvement emerged: updating operational definitions, appraisal of the recruitment process, refinement of data elements, and improvement of data quality and reporting. Recommendations presented were drawn from our collective experience and accumulated knowledge in operating an international registry. These can be readily generalized to other health-related reporting programs beyond clinical registries.

Surgical instrument segmentation in robot-assisted surgery (RAS) - especially that using learning-based models - relies on the assumption that training and testing videos are sampled from the same domain. However, it is impractical and expensive to collect and annotate sufficient data from every new domain. To greatly increase the label efficiency, we explore a new problem, i.e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame. We propose MDAL, a meta-learning based dynamic online adaptive learning scheme with a two-stage framework to fast adapt the model parameters on the first frame and partial subsequent frames while predicting the results. MDAL learns the general knowledge of instruments and the fast adaptation ability through the video-specific meta-learning paradigm. The added gradient gate excludes the noisy supervision from pseudo masks for dynamic online adaptation on target videos. We demonstrate empirically that MDAL outperforms other state-of-the-art methods on two datasets (including a real-world RAS dataset). The promising performance on ex-vivo scenes also benefits the downstream tasks such as robot-assisted suturing and camera control.