Efficient 3D cone trajectory design for improved combined angiographic and perfusion imaging using arterial spin labeling.

PURPOSE: To improve the spatial resolution and repeatability of a non-contrast MRI technique for simultaneous time resolved 3D angiography and perfusion imaging by developing an efficient 3D cone trajectory design. METHODS: A novel parameterized 3D cone trajectory design incorporating the 3D golden angle was integrated into 4D combined angiography and perfusion using radial imaging and arterial spin labeling (CAPRIA) to achieve higher spatial resolution and sampling efficiency for both dynamic angiography and perfusion imaging with flexible spatiotemporal resolution. Numerical simulations and physical phantom scanning were used to optimize the cone design. Eight healthy volunteers were scanned to compare the original radial trajectory in 4D CAPRIA with our newly designed cone trajectory. A locally low rank reconstruction method was used to leverage the complementary k-space sampling across time. RESULTS: The improved sampling in the periphery of k-space obtained with the optimized 3D cone trajectory resulted in improved spatial resolution compared with the radial trajectory in phantom scans. Improved vessel sharpness and perfusion visualization were also achieved in vivo. Less dephasing was observed in the angiograms because of the short TE of our cone trajectory and the improved k-space sampling efficiency also resulted in higher repeatability compared to the original radial approach. CONCLUSION: The proposed 3D cone trajectory combined with 3D golden angle ordering resulted in improved spatial resolution and image quality for both angiography and perfusion imaging and could potentially benefit other applications that require an efficient sampling scheme with flexible spatial and temporal resolution.

Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation.

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 1,986 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that: 1) challenge metric was permissive with the false positive predictions; and 2) size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download.

Hierarchical pathology screening for cervical abnormality.

Cervical smear screening is an imaging-based cancer detection tool, which is of pivotal importance for the early-stage diagnosis. A computer-aided screening system can automatically find out if the scanned whole-slide images (WSI) with cervical cells are classified as "abnormal" or "normal", and then alert pathologists. It can significantly reduce the workload for human experts, and is therefore highly demanded in clinical practice. Most of the screening methods are based on automatic cervical cell detection and classification, but the accuracy is generally limited due to the high variation of cell appearance and lacking context information from the surroundings. Here we propose a novel and hierarchical framework for automatic cervical smear screening aiming at the robust performance of case-level diagnosis and finding suspected "abnormal" cells. Our framework consists of three stages. We commence by extracting a large number of pathology images from the scanned WSIs, and implementing abnormal cell detection to each pathology image. Then, we feed the detected "abnormal" cells with corresponding confidence into our novel classification model for a comprehensive analysis of the extracted pathology images. Finally, we summarize the classification outputs of all extracted images, and determine the overall screening result for the target case. Experiments show that our three-stage hierarchical method can effectively suppress the errors from cell-level detection, and provide an effective and robust way for cervical abnormality screening.