Transferring Adult-like Phase Images for Robust Multi-view Isointense Infant Brain Segmentation.

Accurate tissue segmentation of infant brain in magnetic resonance (MR) images is crucial for charting early brain development and identifying biomarkers. Due to ongoing myelination and maturation, in the isointense phase (6-9 months of age), the gray and white matters of infant brain exhibit similar intensity levels in MR images, posing significant challenges for tissue segmentation. Meanwhile, in the adult-like phase around 12 months of age, the MR images show high tissue contrast and can be easily segmented. In this paper, we propose to effectively exploit adult-like phase images to achieve robustmulti-view isointense infant brain segmentation. Specifically, in one way, we transfer adult-like phase images to the isointense view, which have similar tissue contrast as the isointense phase images, and use the transferred images to train an isointense-view segmentation network. On the other way, we transfer isointense phase images to the adult-like view, which have enhanced tissue contrast, for training a segmentation network in the adult-like view. The segmentation networks of different views form a multi-path architecture that performs multi-view learning to further boost the segmentation performance. Since anatomy-preserving style transfer is key to the downstream segmentation task, we develop a Disentangled Cycle-consistent Adversarial Network (DCAN) with strong regularization terms to accurately transfer realistic tissue contrast between isointense and adult-like phase images while still maintaining their structural consistency. Experiments on both NDAR and iSeg-2019 datasets demonstrate a significant superior performance of our method over the state-of-the-art methods.

Multi-target landmark detection with incomplete images via reinforcement learning and shape prior embedding.

Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets. Based on learning a navigation policy, instead of predicting targets directly, reinforcement learning (RL)-based methods have the potential to tackle this challenge in an efficient manner. Inspired by this, in this work we propose a multi-agent RL framework for simultaneous multi-target landmark detection. This framework is aimed to learn from incomplete or (and) complete images to form an implicit knowledge of global structure, which is consolidated during the training stage for the detection of targets from either complete or incomplete test images. To further explicitly exploit the global structural information from incomplete images, we propose to embed a shape model into the RL process. With this prior knowledge, the proposed RL model can not only localize dozens of targets simultaneously, but also work effectively and robustly in the presence of incomplete images. We validated the applicability and efficacy of the proposed method on various multi-target detection tasks with incomplete images from practical clinics, using body dual-energy X-ray absorptiometry (DXA), cardiac MRI and head CT datasets. Results showed that our method could predict whole set of landmarks with incomplete training images up to 80% missing proportion (average distance error 2.29 cm on body DXA), and could detect unseen landmarks in regions with missing image information outside FOV of target images (average distance error 6.84 mm on 3D half-head CT). Our code will be released via https://zmiclab.github.io/projects.html.

Learning-based algorithms for vessel tracking: A review.

Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.

Directional fast-marching and multi-model strategy to extract coronary artery centerlines.

BACKGROUND: Computed tomography angiography (CTA) is a non-invasive technique to image coronary arteries and evaluate coronary artery diseases (CAD). The diagnosis of CAD requires modeling anatomical structures and analyzing the function and pathology of the coronary arteries. Therefore, a robust and automated method for extracting reliable coronary artery centerlines is valuable in clinical practice. METHOD: We extracted coronary centerlines using the directional fast marching (DFM) method and improved DFM with a multi-model strategy. The method comprises model guidance, the application of vessel direction, and a multi-model strategy: (1) coronary models are constructed using registration techniques and then used as prior knowledge of the vessels; (2) the vessel direction, modified from the eigenvectors of the Hessian matrix and vesselness, is used to guide the search for the vessel points during fast marching; and (3) the multi-model strategy is applied to identify suboptimal results from the overall outcome as in multi-atlas segmentation. Overlap and accuracy metrics are used to assess the segmentation. The authors evaluated the performance of the proposed method on 32 CT cardiac angiography datasets from the Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF). The authors also studied the effect of models on DFM. RESULTS: For the quantitative evaluation, DFM improved the average overlap (OV) from 43.6% of a method without model information to 77.8%. In addition, with the ground truth delineated by experts, multi-model DFM (MM-DFM) obtained 83.5% average overlap (OV) in the training datasets and 86.6% in the test datasets. CONCLUSION: The authors propose a novel approach to extract coronary centerlines from CTA using DFM and further extend DFM to a multi-model strategy. DFM effectively applies the prior shape of the coronary vessels and vascular features within the target image and has the potential to achieve clinically relevant results.