Centerline depth world for left atrial appendage orifice localization using reinforcement learning.

Left atrial appendage (LAA) occlusion (LAAO) is a minimally invasive implant-based method to prevent cardiovascular stroke in patients with non-valvular atrial fibrillation. Assessing the LAA orifice in preoperative CT angiography plays a crucial role in choosing an appropriate LAAO implant size and a proper C-arm angulation. However, accurate orifice localization is hard because of the high anatomic variation of LAA, and unclear position and orientation of the orifice in available CT views.

Bone Suppression on Chest Radiographs for Pulmonary Nodule Detection: Comparison between a Generative Adversarial Network and Dual-Energy Subtraction.

Objective: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs).

Materials And Methods: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR).

Partial Policy-Based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images.

Utilizing the idea of long-term cumulative return, reinforcement learning (RL) has shown remarkable performance in various fields. We follow the formulation of landmark localization in 3D medical images as an RL problem. Whereas value-based methods have been widely used to solve RL-based localization problems, we adopt an actor-critic based direct policy search method framed in a temporal difference learning approach.

Deep vessel segmentation by learning graphical connectivity.

We propose a novel deep learning based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without consideration of the graphical structure of vessel shape. Effective use of the strong relationship that exists between vessel neighborhoods can help improve the vessel segmentation accuracy.

Fast Adaptive RNN Encoder⁻Decoder for Anomaly Detection in SMD Assembly Machine.

Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder⁻Decoder with operating machine sounds.

Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images.

We propose a framework for localization and classification of masses in breast ultrasound images. We have experimentally found that training convolutional neural network-based mass detectors with large, weakly annotated datasets presents a non-trivial problem, while overfitting may occur with those trained with small, strongly annotated datasets. To overcome these problems, we use a weakly annotated dataset together with a smaller strongly annotated dataset in a hybrid manner.

Automatic aortic valve landmark localization in coronary CT angiography using colonial walk.

The minimally invasive transcatheter aortic valve implantation (TAVI) is the most prevalent method to treat aortic valve stenosis. For pre-operative surgical planning, contrast-enhanced coronary CT angiography (CCTA) is used as the imaging technique to acquire 3-D measurements of the valve. Accurate localization of the eight aortic valve landmarks in CT images plays a vital role in the TAVI workflow because a small error risks blocking the coronary circulation.

Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound.

Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive.

Prognostication of cardiac arrest survivors using low apparent diffusion coefficient cluster volume.

Introduction: We developed a new neuroprognostication method for cardiac arrest (CA) using the relative volume of the most dominant cluster of low apparent diffusion coefficient (ADC) voxels and tested its performance in a multicenter setting.

Methods: Adult (>15 years) out-of-hospital CA patients from three different facilities who underwent an MRI 12h after resuscitation were retrospectively analyzed. Patients with unknown long-term prognosis or poor baseline neurologic function were excluded.

A Novel Cascade Classifier for Automatic Microcalcification Detection.

In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii) a more complex discriminative restricted Boltzmann machine (DRBM) classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii) a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases.

Forest Walk Methods for Localizing Body Joints from Single Depth Image.

We present multiple random forest methods for human pose estimation from single depth images that can operate in very high frame rate. We introduce four algorithms: random forest walk, greedy forest walk, random forest jumps, and greedy forest jumps. The proposed approaches can accurately infer the 3D positions of body joints without additional information such as temporal prior.

Structured patch model for a unified automatic and interactive segmentation framework.

We present a novel interactive segmentation framework incorporating a priori knowledge learned from training data. The knowledge is learned as a structured patch model (StPM) comprising sets of corresponding local patch priors and their pairwise spatial distribution statistics which represent the local shape and appearance along its boundary and the global shape structure, respectively. When successive user annotations are given, the StPM is appropriately adjusted in the target image and used together with the annotations to guide the segmentation.

Low apparent diffusion coefficient cluster-based analysis of diffusion-weighted MRI for prognostication of out-of-hospital cardiac arrest survivors.

Objective: Recent studies suggested quantitative analysis of diffusion-weighted magnetic resonance imaging as a promising tool for early prognostication of cardiac arrest patients. However, most of their methods involve significant manual image handling often subjective and difficult to reproduce. Therefore developing a computerized analysis method using easy-to-define characteristics would be useful.

Data-driven interactive 3D medical image segmentation based on structured patch model.

In this paper, we present a novel three dimensional interactive medical image segmentation method based on high level knowledge of training set. Since the interactive system should provide intermediate results to an user quickly, insufficient low level models are used for most of previous methods. To exploit the high level knowledge within a short time, we construct a structured patch model that consists of multiple corresponding patch sets.

NONRIGID VOLUME REGISTRATION USING SECOND-ORDER MRF MODEL.

In this paper, we introduce a nonrigid registration method using a Markov Random Field (MRF) energy model with second-order smoothness priors. The registration determines an optimal labeling of the MRF energy model where the label corresponds to a 3D displacement vector. In the MRF energy model, spatial relationships are constructed between nodes using second-order smoothness priors.

JPEG2000 2D and 3D reversible compressions of thin-section chest CT images: improving compressibility by increasing data redundancy outside the body region.

Purpose: To propose a preprocessing technique that increases the compressibility in reversible compressions of thin-section chest computed tomographic (CT) images and to measure the increase in compression ratio (CR) in Joint Photographic Experts Group (JPEG) 2000 two-dimensional (2D) and three-dimensional (3D) compressions.

Materials And Methods: This study had institutional review board approval, with waiver of informed patient consent. A preprocessing technique that automatically segments pixels outside the body region and replaces their values with a constant value to maximize data redundancy was developed.

Rolled fingerprint construction using MRF-based nonrigid image registration.

This paper proposes a new rolled fingerprint construction approach incorporating a state-of-the-art nonrigid image registration method based upon a Markov random field (MRF) energy model. The proposed method finds dense correspondences between images from a rolled fingerprint sequence and warps the entire fingerprint area to synthesize a rolled fingerprint. This method can generate conceptually more accurate rolled fingerprints by preserving the geometric properties of the finger surface as opposed to ink-based rolled impressions and other existing rolled fingerprint construction methods.

Partition-based extraction of cerebral arteries from CT angiography with emphasis on adaptive tracking.

In this paper a method to extract cerebral arteries from computed tomographic angiography (CTA) is proposed. Since CTA shows both bone and vessels, the examination of vessels is a difficult task. In the upper part of the brain, the arteries of main interest are not close to bone and can be well segmented out by thresholding and simple connected-component analysis.

Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: particle filtering.

In this paper a method to extract cerebral arterial segments from CT angiography (CTA) is proposed. The segmentation of cerebral arteries in CTA is a challenging task mainly due to bone contact and vein contamination. The proposed method considers a vessel segment as an ellipse travelling in three-dimensional (3D) space and segments it out by tracking the ellipse in spatial sequence.