Freehand 3D Ultrasound Imaging Based on Probe-mounted Vision and IMU System.

OBJECTIVES: Freehand three-dimensional (3D) ultrasound (US) is of great significance for clinical diagnosis and treatment, it is often achieved with the aid of external devices (optical and/or electromagnetic, etc.) that monitor the location and orientation of the US probe. However, this external monitoring is often impacted by imaging environment such as optical occlusions and/or electromagnetic (EM) interference. METHODS: To address the above issues, we integrated a binocular camera and an inertial measurement unit (IMU) on a US probe. Subsequently, we built a tight coupling model utilizing the unscented Kalman algorithm based on Lie groups (UKF-LG), combining vision and inertial information to infer the probe's movement, through which the position and orientation of the US image frame are calculated. Finally, the volume data was reconstructed with the voxel-based hole-filling method. RESULTS: The experiments including calibration experiments, tracking performance evaluation, phantom scans, and real scenarios scans have been conducted. The results show that the proposed system achieved the accumulated frame position error of 3.78 mm and the orientation error of 0.36° and reconstructed 3D US images with high quality in both phantom and real scenarios. CONCLUSIONS: The proposed method has been demonstrated to enhance the robustness and effectiveness of freehand 3D US. Follow-up research will focus on improving the accuracy and stability of multi-sensor fusion to make the system more practical in clinical environments.

Sub-high amylose maize starch: an ideal substrate to generate starch with lower digestibility by fermentation of Qu.

BACKGROUND: The fermentation of Qu (FQ) is a novel method to modify the properties of starch to expand its application and especially to increase the resistant starch (RS) content. Using waxy maize starch (WMS) as a fermentation substrate can increase the RS content significantly but it may be time consuming and not cost effective due to the almost negligible RS content of WMS. To solve this problem, we hypothesized that sub-high amylose starch (s-HAMS), with an amylose content close to 50% could be an ideal substrate for FQ. RESULTS: The results showed that FQ did not change the shape and the particle size of starch granules, the gelatinization peak (Tp), or the conclusion temperature (Tc), but the slowly digested starch content declined. Rapidly digested starch content fluctuated during FQ and the amylose content decreased within 36 h and then increased. Within 24h, FQ significanlty increased these values: the RS content, relative crystallinity (RC), the ratio of FTIR absorbances at 1047/1022cm-1, the diffraction peak at 19.8° in X-ray diffraction (XRD), and the gelatinization onset temperature (To) increased significantly, within 24 h of FQ. However, after 24 h of fermentation, the RS content, RC, the ratio of FTIR absorbances at 1047/1022 cm-1, and gelatinization enthalpy (ΔH) decreased significantly. CONCLUSION: Sub-high amylose starch is more suitable for FQ to produce low digestibility starch, and the increase in RS may be due to the formation of 'amylose-lipid' complexes (RS5). © 2024 Society of Chemical Industry.

Genetic architecture of ear traits based on association mapping and co-expression networks in maize inbred lines and hybrids.

Ear traits are key contributors to grain yield in maize; therefore, exploring their genetic basis facilitates the improvement of grain yield. However, the underlying molecular mechanisms of ear traits remain obscure in both inbred lines and hybrids. Here, two association panels, respectively, comprising 203 inbred lines (IP) and 246 F1 hybrids (HP) were employed to identify candidate genes for six ear traits. The IP showed higher phenotypic variation and lower phenotypic mean than the HP for all traits, except ear tip-barrenness length. By conducting a genome-wide association study (GWAS) across multiple environments, 101 and 228 significant single-nucleotide polymorphisms (SNPs) associated with six ear traits were identified in the IP and HP, respectively. Of these significant SNPs identified in the HP, most showed complete-incomplete dominance and over-dominance effects for each ear trait. Combining a gene co-expression network with GWAS results, 186 and 440 candidate genes were predicted in the IP and HP, respectively, including known ear development genes ids1 and sid1. Of these, nine candidate genes were detected in both populations and expressed in maize ear and spikelet tissues. Furthermore, two key shared genes (GRMZM2G143330 and GRMZM2G171139) in both populations were found to be significantly associated with ear traits in the maize Goodman diversity panel with high-density variations. These findings advance our knowledge of the genetic architecture of ear traits between inbred lines and hybrids and provide a valuable resource for the genetic improvement of ear traits in maize. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01426-9.

Identification of the Maize LEA Gene Family and Its Relationship with Kernel Dehydration.

Maize, the most widely planted and highest yielding of the three major crops in the world, requires the development and breeding of new varieties to accommodate the shift towards mechanized harvesting. However, the moisture content of kernels during harvest poses a significant challenge to mechanized harvesting, leading to seed breakage and increased storage costs. Previous studies highlighted the importance of LEA (Late Embryogenesis Abundant) members in regulating kernel dehydration. In this study, we aimed to gain a better understanding of the relationship between the LEA family and grain dehydration in maize. Through expression pattern analysis of maize, we identified 52 LEA genes (ZmLEAs) distributed across 10 chromosomes, organized into seven subgroups based on phylogenetic analysis, gene structure, and conserved motifs. Evolutionary and selective pressure analysis revealed that the amplification of ZmLEA genes primarily resulted from whole-genome or fragment replication events, with strong purifying selection effects during evolution. Furthermore, the transcriptome data of kernels of two maize inbred lines with varying dehydration rates at different developmental stages showed that 14 ZmLEA genes were expressed differentially in the two inbreds. This suggested that the ZmLEA genes might participate in regulating the kernel dehydration rate (KDR) in maize. Overall, this study enhances our understanding of the ZmLEA family and provides a foundation for further research into its role in regulating genes associated with grain dehydration in maize.

Effect of scattered megavoltage x-rays on markerless tumor tracking using dual energy kilovoltage imaging.

PURPOSE: To determine the effect of megavoltage (MV) scatter on the accuracy of markerless tumor tracking (MTT) for lung tumors using dual energy (DE) imaging and to consider a post-processing technique to mitigate the effects of MV scatter on DE-MTT. METHODS: A Varian TrueBeam linac was used to acquire a series of interleaved 60/120 kVp images of a motion phantom with simulated tumors (10 and 15 mm diameter). Two sets of consecutive high/low energy projections were acquired, with and without MV beam delivery. The MV field sizes (FS) ranged from 2 × 2 cm2 -6 × 6 cm2 in steps of 1 × 1 cm2 . Weighted logarithmic subtraction was performed on sequential images to produce soft-tissue images for kV only (DEkV ) and kV with MV beam on (DEkV+MV ). Wavelet and fast Fourier transformation filtering (wavelet-FFT) was used to remove stripe noise introduced by MV scatter in the DE images ( DE kV + MV Corr ${\rm{DE}}_{{\rm{kV}} + {\rm{MV}}}^{{\rm{Corr}}}$ ). A template-based matching algorithm was then used to track the target on DEkV, DEkV+MV , and DE kV + MV Corr ${\rm{DE}}_{{\rm{kV}} + {\rm{MV}}}^{{\rm{Corr}}}$ images. Tracking accuracy was evaluated using the tracking success rate (TSR) and mean absolute error (MAE). RESULTS: For the 10 and 15 mm targets, the TSR for DEkV images was 98.7% and 100%, and MAE was 0.53 and 0.42 mm, respectively. For the 10 mm target, the TSR, including the effects of MV scatter, ranged from 86.5% (2 × 2 cm2 ) to 69.4% (6 × 6 cm2 ), while the MAE ranged from 2.05 mm to 4.04 mm. The application of wavelet-FFT algorithm to remove stripe noise ( DE kV + MV Corr ${\rm{DE}}_{{\rm{kV}} + {\rm{MV}}}^{{\rm{Corr}}}$ ) resulted in TSR values of 96.9% (2 × 2 cm2 ) to 93.4% (6 × 6 cm2 ) and subsequent MAE values were 0.89 mm to 1.37 mm. Similar trends were observed for the 15 mm target. CONCLUSION: MV scatter significantly impacts the tracking accuracy of lung tumors using DE images. Wavelet-FFT filtering can improve the accuracy of DE-MTT during treatment.

Effect of different noise reduction techniques and template matching parameters on markerless tumor tracking using dual-energy imaging.

PURPOSE: To evaluate the impact of various noise reduction algorithms and template matching parameters on the accuracy of markerless tumor tracking (MTT) using dual-energy (DE) imaging. METHODS: A Varian TrueBeam linear accelerator was used to acquire a series of alternating 60 and 120 kVp images (over a 180° arc) using fast kV switching, on five early-stage lung cancer patients. Subsequently, DE logarithmic weighted subtraction was performed offline on sequential images to remove bone. Various noise reduction techniques-simple smoothing, anticorrelated noise reduction (ACNR), noise clipping (NC), and NC-ACNR-were applied to the resultant DE images. Separately, tumor templates were generated from the individual planning CT scans, and band-pass parameter settings for template matching were varied. Template tracking was performed for each combination of noise reduction techniques and templates (based on band-pass filter settings). The tracking success rate (TSR), root mean square error (RMSE), and missing frames (percent unable to track) were evaluated against the estimated ground truth, which was obtained using Bayesian inference. RESULTS: DE-ACNR, combined with template band-pass filter settings of σlow  = 0.4 mm and σhigh  = 1.6 mm resulted in the highest TSR (87.5%), RMSE (1.40 mm), and a reasonable amount of missing frames (3.1%). In comparison to unprocessed DE images, with optimized band-pass filter settings of σlow  = 0.6 mm and σhigh  = 1.2 mm, the TSR, RMSE, and missing frames were 85.3%, 1.62 mm, and 2.7%, respectively. Optimized band-pass filter settings resulted in improved TSR values and a lower missing frame rate for both unprocessed DE and DE-ACNR as compared to the use previously published band-pass parameters based on single energy kV images. CONCLUSION: Noise reduction strategies combined with the optimal selection of band-pass filter parameters can improve the accuracy and TSR of MTT for lung tumors when using DE imaging.

An efficient deep equilibrium model for medical image segmentation.

In this paper, we propose an effective method that takes the advantages of classical methods and deep learning technology for medical image segmentation through modeling the neural network as a fixed point iteration seeking for system equilibrium by adding a feedback loop. In particular, the nuclear segmentation of medical image is used as an example to demonstrate the proposed method where it can successfully complete the challenge of segmenting nuclei from cells in different histopathological images. Specifically, the nuclei segmentation is formulated as a dynamic process to search for the system equilibrium. Starting from an initial segmentation generated either by a classic algorithm or pre-trained deep learning model, a sequence of segmentation output is created and combined with the original image to dynamically drive the segmentation towards the expected value. This dynamical extension to neural networks requires little extra change on the backbone deep neural network while it significantly increased model accuracy, generalizability, and stability as demonstrated by intensive experimental results from pathological images of different tissue types across different open datasets.

Characterization of Markerless Tumor Tracking Using the On-Board Imager of a Commercial Linear Accelerator Equipped With Fast-kV Switching Dual-Energy Imaging.

PURPOSE: To describe and characterize fast-kV switching, dual-energy (DE) imaging implemented within the on-board imager of a commercial linear accelerator for markerless tumor tracking (MTT). METHODS AND MATERIALS: Fast-kV switching, DE imaging provides for rapid switching between programmed tube voltages (ie, 60 and 120 kVp) from one image frame to the next. To characterize this system, the weighting factor used for logarithmic subtraction and signal difference-to-noise ratio were analyzed as a function of time and frame rate. MTT was evaluated using a thorax motion phantom and fast kV, DE imaging was compared versus single energy (SE) imaging over 360 degrees of rotation. A template-based matching algorithm was used to track target motion on both DE and SE sequences. Receiver operating characteristics were used to compare tracking results for both modalities. RESULTS: The weighting factor was inversely related to frame rate and stable over time. After applying the frame rate-dependent weighting factor, the signal difference-to-noise ratio was consistent across all frame rates considered for simulated tumors ranging from 5 to 25 mm in diameter. An analysis of receiver operating characteristics curves showed improved tracking with DE versus SE imaging. The area under the curve for the 10-mm target ranged from 0.821 to 0.858 for SE imaging versus 0.968 to 0.974 for DE imaging. Moreover, the residual tracking errors for the same target size ranged from 2.02 to 2.18 mm versus 0.79 to 1.07 mm for SE and DE imaging, respectively. CONCLUSIONS: Fast-kV switching, DE imaging was implemented on the on-board imager of a commercial linear accelerator. DE imaging resulted in improved MTT accuracy over SE imaging. Such an approach may have application for MTT of patients with lung cancer receiving stereotactic body radiation therapy, particularly for small tumors where MTT with SE imaging may fail.

Adaptive weighted log subtraction based on neural networks for markerless tumor tracking using dual-energy fluoroscopy.

PURPOSE: To present a novel method, based on convolutional neural networks (CNN), to automate weighted log subtraction (WLS) for dual-energy (DE) fluoroscopy to be used in conjunction with markerless tumor tracking (MTT). METHODS: A CNN was developed to automate WLS (aWLS) of DE fluoroscopy to enhance soft tissue visibility. Briefly, this algorithm consists of two phases: training a CNN architecture to predict pixel-wise weighting factors followed by application of WLS subtraction to reduce anatomical noise. To train the CNN, a custom phantom was built consisting of aluminum (Al) and acrylic (PMMA) step wedges. Per-pixel ground truth (GT) weighting factors were calculated by minimizing the contrast of Al in the step wedge phantom to train the CNN. The pretrained model was then utilized to predict pixel-wise weighting factors for use in WLS. For comparison, the weighting factor was manually determined in each projection (mWLS). A thorax phantom with five simulated spherical targets (5-25 mm) embedded in a lung cavity, was utilized to assess aWLS performance. The phantom was imaged with fast-kV dual-energy (120 and 60 kVp) fluoroscopy using the on-board imager of a commercial linear accelerator. DE images were processed offline to produce soft tissue images using both WLS methods. MTT was compared using soft tissue images produced with both mWLS and aWLS techniques. RESULTS: Qualitative evaluation demonstrated that both methods achieved soft tissue images with similar quality. The use of aWLS increased the number of tracked frames by 1-5% compared to mWLS, with the largest increase observed for the smallest simulated tumors. The tracking errors for both methods produced agreement to within 0.1 mm. CONCLUSIONS: A novel method to perform automated WLS for DE fluoroscopy was developed. Having similar soft tissue quality as well as bone suppression capability as mWLS, this method allows for real-time processing of DE images for MTT.

Markerless tumor tracking using fast-kV switching dual-energy fluoroscopy on a benchtop system.

PURPOSE: To evaluate markerless tumor tracking (MTT) using fast-kV switching dual-energy (DE) fluoroscopy on a bench top system. METHODS: Fast-kV switching DE fluoroscopy was implemented on a bench top which includes a turntable stand, flat panel detector, and x-ray tube. The customized generator firmware enables consecutive x-ray pulses that alternate between programmed high and low energies (e.g., 60 and 120 kVp) with a maximum frame rate of 15 Hz. In-house software was implemented to perform weighted DE subtraction of consecutive images to create an image sequence that removes bone and enhances soft tissues. The weighting factor was optimized based on gantry angle. To characterize this system, a phantom was used that simulates the chest anatomy and tumor motion in the lung. Five clinically relevant tumor sizes (5-25 mm diameter) were considered. The targets were programmed to move in the inferior-superior direction of the phantom, perpendicular to the x-ray beam, using a cos4 waveform to mimic respiratory motion. Target inserts were then tracked with MTT software using a template matching method. The optimal computed tomography (CT) slice thickness for template generation was also evaluated. Tracking success rate and accuracy were calculated in regions of the phantom where the target overlapped ribs vs spine, to compare the performance of single energy (SE) and DE imaging methods. RESULTS: For the 5 mm target, a CT slice thickness of 0.75 mm resulted in the lowest tracking error. For the larger targets (≥10 mm) a CT slice thickness ≤2 mm resulted in comparable tracking errors for SE and DE images. Overall DE imaging improved MTT accuracy, relative to SE imaging, for all tumor targets in a rotational acquisition. Compared to SE, DE imaging increased tracking success rate of small target inserts (5 and 10 mm). For fast motion tracking, success rates improved from 23% to 64% and 74% to 90% for 5 and 10 mm targets inserts overlapping ribs, respectively. For slow moving targets success rates improved from 19% to 59% and 59% to 91% in 5 and 10 mm targets overlapping the ribs, respectively. Similar results were observed when the targets overlapped the spine. For larger targets (≥15 mm) tracking success rates were comparable using SE and DE imaging. CONCLUSION: This work presents the first results of MTT using fast-kV switching DE fluoroscopy. Using DE imaging has improved the tracking accuracy of MTT, especially for small targets. The results of this study will guide the future implementation of fast-kV switching DE imaging using the on-board imager of a linear accelerator.

Dual edge apparatus and algorithm for measurement of x-ray beam spot parameters.

PURPOSE: The shape, size, and location of the x-ray beam spot (where the electron beam strikes the target) in a linac-based radiation therapy machine are of potential clinical significance. Established techniques to measure the beam spot parameters involve specialized hardware and typically assess size and shape of the beam spot or its position, but not both. A simple apparatus and algorithm for measuring all beam spot parameters simultaneously is proposed here. METHODS: The apparatus is composed of two partially transmitting edge plates mounted at different vertical positions. The mount for the apparatus slides into the accessory tray of the linac treatment head so that it rotates with the collimator, and it is imaged by the existing electronic portal imaging device (EPID) over multiple collimator angles. A software algorithm takes the acquired images and uses a parallel-beam CT reconstruction technique to compute beam spot size, shape, and position in one computation. In addition, the wobble of the collimator assembly can be estimated. The overall method was validated with both Monte Carlo simulation and with comparison to in-house spot camera measurements on a radiation therapy system. RESULTS: The algorithm correctly predicted the beam spot parameters used for the Monte Carlo simulation to better than 50 µm accuracy in all cases. Furthermore, results from the dual edge method matched spot camera results with 30 µm accuracy for beam spot size and shape, with 80 µm average accuracy for beam spot position, and better than 200 µm accuracy for collimator assembly wobble. CONCLUSIONS: We have developed a combination dual edge apparatus and image processing algorithm that, when used on a radiotherapy linac with an EPID, can accurately determine the size and shape of the electron beam spot, its position relative to collimator rotation axis, and the wobble of the collimator assembly.

Multi-scale learning based segmentation of glands in digital colonrectal pathology images.

Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation. After the learning step, the dictionaries are used collectively to perform the classification and segmentation for the new image.

Hierarchical nucleus segmentation in digital pathology images.

Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set.

Graph Curvature for Differentiating Cancer Networks.

Cellular interactions can be modeled as complex dynamical systems represented by weighted graphs. The functionality of such networks, including measures of robustness, reliability, performance, and efficiency, are intrinsically tied to the topology and geometry of the underlying graph. Utilizing recently proposed geometric notions of curvature on weighted graphs, we investigate the features of gene co-expression networks derived from large-scale genomic studies of cancer. We find that the curvature of these networks reliably distinguishes between cancer and normal samples, with cancer networks exhibiting higher curvature than their normal counterparts. We establish a quantitative relationship between our findings and prior investigations of network entropy. Furthermore, we demonstrate how our approach yields additional, non-trivial pair-wise (i.e. gene-gene) interactions which may be disrupted in cancer samples. The mathematical formulation of our approach yields an exact solution to calculating pair-wise changes in curvature which was computationally infeasible using prior methods. As such, our findings lay the foundation for an analytical approach to studying complex biological networks.

A Kalman Filtering Perspective for Multiatlas Segmentation.

In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity-neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.

Interactive Image Segmentation Framework Based On Control Theory.

Segmentation of anatomical structures in medical imagery is a key step in a variety of clinical applications. Designing a generic, automated method that works for various structures and imaging modalities is a daunting task. Instead of proposing a new specific segmentation algorithm, in this paper, we present a general design principle on how to integrate user interactions from the perspective of control theory. In this formulation, Lyapunov stability analysis is employed to design and analyze an interactive segmentation system. The effectiveness and robustness of the proposed method are demonstrated.

Optimal-mass-transfer-based estimation of glymphatic transport in living brain.

It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the 'glymphatic pathway' plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs1,2. It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through para-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach3 to derive the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data.

Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport.

Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only a few time points, limiting the study of continuous disease/treatment progression. Having the ability to produce a sensible time interpolation of the data can lead to improved analysis, such as intuitive visualizations of anatomical changes, or the creation of more samples to improve statistical analysis. In this work, we model interpolation of medical image data, in particular shape data, using the theory of optimal mass transport (OMT), which can construct a continuous transition from two time points while preserving "mass" (e.g., image intensity, shape volume) during the transition. The theory even allows a short extrapolation in time and may help predict short-term treatment impact or disease progression on anatomical structure. We apply the proposed method to the hippocampus-amygdala complex in schizophrenia, the heart in atrial fibrillation, and full head MR images in traumatic brain injury.

Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery.

Desorption electrospray ionization mass spectrometry (DESI-MS) provides a highly sensitive imaging technique for differentiating normal and cancerous tissue at the molecular level. This can be very useful, especially under intra-operative conditions where the surgeon has to make crucial decision about the tumor boundary. In such situations, the time it takes for imaging and data analysis becomes a critical factor. Therefore, in this work we utilize compressive sensing to perform the sparse sampling of the tissue, which halves the scanning time. Furthermore, sparse feature selection is performed, which not only reduces the dimension of data from about 104 to less than 50, and thus significantly shortens the analysis time. This procedure also identifies biochemically important molecules for pathological analysis. The methods are validated on brain and breast tumor data sets.

Automatic segmentation of the left atrium from MR images via variational region growing with a moments-based shape prior.

The planning and evaluation of left atrial ablation procedures are commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery. The segmentation problem is formulated as a problem in variational region growing. In particular, the method starts locally by searching for a seed region of the left atrium from an MR slice. A global constraint is imposed by applying a shape prior to the left atrium represented by Zernike moments. The overall growing process is guided by the robust statistics of intensities from the seed region along with the shape prior to capture the entire atrial region. The robustness and accuracy of our approach are demonstrated by experimental results from 64 human MR images.