4D CNN for semantic segmentation of cardiac volumetric sequences
This addresses the challenge of accurate and consistent segmentation in medical imaging for cardiac analysis, but it is incremental as it builds on existing 3D methods with a 4D extension.
The paper tackled the problem of segmenting cardiac volumetric sequences from CT scans by proposing a 4D CNN that uses a sparse loss function to handle limited annotations, achieving temporally consistent segmentations and demonstrating feasibility on cardiac 4D CCTA data.
We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time. While only a small subset of volumes in the temporal sequence is annotated, we define a sparse loss function on available labels to allow the network to leverage unlabeled images during training and generate a fully segmented sequence. We investigate the accuracy of the proposed 4D network to predict temporally consistent segmentations and compare with traditional 3D segmentation approaches. We demonstrate the feasibility of the 4D CNN and establish its performance on cardiac 4D CCTA.