Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W, editors. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. Cham: Springer International Publishing; 2016. pp. 158-65. [DOI: 10.1007/978-3-319-46726-9_19]

A deep learning approach with temporal consistency for automatic myocardial segmentation of quantitative myocardial contrast echocardiography

Quantitative myocardial contrast echocardiography (MCE) has been proved to be valuable in detecting myocardial ischemia. During quantitative MCE analysis, myocardial segmentation is a critical step in determining accurate region of interests (ROIs). However, traditional myocardial segmentation mainly relies on manual tracing of myocardial contours, which is time-consuming and laborious. To solve this problem, we propose a fully automatic myocardial segmentation framework that can segment myocardial regions in MCE accurately without human intervention. A total of 100 patients' MCE sequences were divided into a training set and a test set according to a 7: 3 proportion for analysis. We proposed a bi-directional training schema, which incorporated temporal information of forward and backward direction among frames in MCE sequences to ensure temporal consistency by combining convolutional neural network with recurrent neural network. Experiment results demonstrated that compared with a traditional segmentation model (U-net) and the model considering only forward temporal information (U-net + forward), our framework achieved the highest segmentation precision in Dice coefficient (U-net vs U-net + forward vs our framework: 0.78 ± 0.07 vs 0.79 ± 0.07 vs 0.81 ± 0.07, p < 0.01), Intersection over Union (0.65 ± 0.09 vs 0.66 ± 0.09 vs 0.68 ± 0.09, p < 0.01), and lowest Hausdorff Distance (32.68 ± 14.6 vs 28.69 ± 13.18 vs 27.59 ± 12.82 pixel point, p < 0.01). In the visual grading study, the performance of our framework was the best among these three models (52.47 ± 4.29 vs 54.53 ± 5.10 vs 57.30 ± 4.73, p < 0.01). A case report on a randomly selected subject for perfusion analysis showed that the perfusion parameters generated by using myocardial segmentation of our proposed framework were similar to that of the expert annotation. The proposed framework could generate more precise myocardial segmentation when compared with traditional methods. The perfusion parameters generated by these myocardial segmentations have a good similarity to that of manual annotation, suggesting that it has the potential to be utilized in routine clinical practice.

Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model

Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to data suffering from large intensity variations. In this paper, we demonstrate how to overcome the above limitations of classic RF by presenting a fully automatic segmentation pipeline for myocardial segmentation in full-cycle 2-D MCE data. Specifically, a statistical shape model is used to provide shape prior information that guide the RF segmentation in two ways. First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to refine and constrain the final segmentation to plausible myocardial shapes. We further improve the performance by introducing a bounding box detection algorithm as a preprocessing step in the segmentation pipeline. Our approach on 2-D image is further extended to 2-D+t sequences which ensures temporal consistency in the final sequence segmentations. When evaluated on clinical MCE data sets, our proposed method achieves notable improvement in segmentation accuracy and outperforms other state-of-the-art methods, including the classic RF and its variants, active shape model and image registration.

Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration

Whole body oncological screening using CT images requires a good anatomical localisation of organs and the skeleton. While a number of algorithms for multi-organ localisation have been presented, developing algorithms for a dense anatomical annotation of the whole skeleton, however, has not been addressed until now. Only methods for specialised applications, e.g., in spine imaging, have been previously described. In this work, we propose an approach for localising and annotating different parts of the human skeleton in CT images. We introduce novel anatomical trilateration features and employ them within iterative scale-adaptive random forests in a hierarchical fashion to annotate the whole skeleton. The anatomical trilateration features provide high-level long-range context information that complements the classical local context-based features used in most image segmentation approaches. They rely on anatomical landmarks derived from the previous element of the cascade to express positions relative to reference points. Following a hierarchical approach, large anatomical structures are segmented first, before identifying substructures. We develop this method for bone annotation but also illustrate its performance, although not specifically optimised for it, for multi-organ annotation. Our method achieves average dice scores of 77.4 to 85.6 for bone annotation on three different data sets. It can also segment different organs with sufficient performance for oncological applications, e.g., for PET/CT analysis, and its computation time allows for its use in clinical practice.