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

arXiv: https://arxiv.org/abs/1806.07497 · HTML: https://mlsanity.com/paper/1806.07497/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [Contrastive Learning for View Classification of Echocardiograms](https://mlsanity.com/paper/2108.03124/) (2021)
- [Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography](https://mlsanity.com/paper/2109.06909/) (2021)
- [TreeFormer: Single-view Plant Skeleton Estimation via Tree-constrained Graph Generation](https://mlsanity.com/paper/2411.16132/) (2024)
- [Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation](https://mlsanity.com/paper/2505.09564/) (2025)

## Abstract (arXiv)

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 2D 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 2D image is further extended to 2D+t sequence which ensures temporal consistency in the resultant sequence segmentations. When evaluated on clinical MCE data, 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.