Chenhui Zhao

Chenhui Zhao, Liyue Shen

Precision medicine, such as patient-adaptive treatments assisted by medical image analysis, poses new challenges for segmentation algorithms in adapting to new patients, due to the large variability across different patients and the limited availability of annotated data for each patient. In this work, we propose a data-efficient segmentation algorithm, namely Part-aware Prompted Segment Anything Model ($P^2SAM$). Without any model fine-tuning, $P^2SAM$ enables seamless adaptation to any new patients relying only on one-shot patient-specific data. We introduce a novel part-aware prompt mechanism to select multiple-point prompts based on the part-level features of the one-shot data, which can be extensively integrated into different promptable segmentation models, such as SAM and SAM 2. Moreover, to determine the optimal number of parts for each specific case, we propose a distribution-guided retrieval approach that further enhances the robustness of the part-aware prompt mechanism. $P^2SAM$ improves the performance by +8.0% and +2.0% mean Dice score for two different patient-adaptive segmentation applications, respectively. In addition, $P^2SAM$ also exhibits impressive generalizability in other adaptive segmentation tasks in the natural image domain, e.g., +6.4% mIoU within personalized object segmentation task. The code is available at: https://github.com/Zch0414/p2sam

Chenhui Zhao, Yiwei Lyu, Asadur Chowdury et al.

The scalability of current language-image pre-training for 3D medical imaging, such as CT and MRI, is constrained by the need for radiologists to manually curate raw clinical studies. In this work, we pioneer pre-training directly on uncurated studies, which both aligns more closely with the radiologist's workflow and provides a natural path to scalability. However, the unique structure of such data presents new challenges for existing model architectures, which were originally designed for 2D slices or single 3D scans. To address this, we introduce a novel hierarchical attention mechanism inspired by the intrinsic hierarchy of radiology data: slice, scan, and study. We denote our framework as Hierarchical attention for Language-Image Pre-training (HLIP). Trained on 220K studies with 3.13 million scans for brain MRI and 240K studies with 1.44 million scans for head CT, HLIP achieves state-of-the-art performance, e.g., +10.5% balanced ACC on the proposed publicly available brain MRI benchmark Pub-Brain-5; +8.3% and +1.7% macro AUC on head CT benchmarks CQ500 and RSNA, respectively. HLIP also exhibits strong generalizability on existing 3D medical language-image pre-training benchmarks, e.g., +4.3% macro AUC on the Rad-ChestCT benchmark when pre-trained on CT-RATE. These results demonstrate that, with HLIP, directly pre-training on uncurated clinical datasets is a scalable and effective direction for language-image pre-training in 3D medical imaging. The code is available at https://github.com/Zch0414/hlip.

Chenhui Zhao, Yan Jiang, Todd C. Hollon

In this work, we extend the SEEDS superpixel algorithm from 2D images to 3D volumes, resulting in 3D SEEDS, a faster, better, and open-source supervoxel algorithm for medical image analysis. We compare 3D SEEDS with the widely used supervoxel algorithm SLIC on 13 segmentation tasks across 10 organs. 3D SEEDS accelerates supervoxel generation by a factor of 10, improves the achievable Dice score by +6.5%, and reduces the under-segmentation error by -0.16%. The code is available at https://github.com/Zch0414/3d_seeds