0391

View Abstract / View Presentation

Deep learning-based thoracic cavity segmentation for hyperpolarized 129Xe MRI

Suphachart Leewiwatwong¹, Junlan Lu², David Mummy³, Isabelle Dummer^3,4, Kevin Yarnall⁵, Ziyi Wang¹, and Bastiaan Driehuys^1,2,3
¹Biomedical Engineering, Duke University, Durham, NC, United States, ²Medical Physics, Duke University, Durham, NC, United States, ³Radiology, Duke University, Durham, NC, United States, ⁴Bioengineering, McGill University, Montréal, QC, Canada, ⁵Mechanical Engineering and Materials Science, Duke University, Durham, NC, United States

We demonstrate a 3D convolutional neural network that facilitates quantitative image analysis by providing a thoracic cavity segmentation using only the ¹²⁹Xe MRI. The CNN achieves a Dice score of 0.955 vs. the gold standard expert reader segmentation.

Figure 4. Segmentations from different models. For each model, the 4 best (left) and worst (right) segmentations are shown with Dice score/SNR (these were calculated from the whole 3D volume). The models’ segmentations are shown in red, those from the expert readers in blue, and their overlaps in purple. White arrows indicate areas where models deviated from ground truth. From this comparison, the model trained with registered data performs better than unprocessed data but struggles with low-SNR and poor ventilation images. Our proposed method (model 3 & 4) helps solve this problem.

Figure 5. Box plot and table of Dice score for each model tested with the “pristine” test dataset. The orange lines indicate the median of the score while the dash green lines indicate the mean. By comparing model 1 and 2, the plot shows the improvement in using the registered dataset for training 3D-CNN but with a drawback shown by low-score outliers due to poor ventilation and low-SNR images. Our proposed method (model 3 & 4) solves the problem and eliminates these outliers.