Automatic segmentation of mediastinal pathological lymph nodes with Deep Learning: the importance of accurate manual segmentations as ground truths

Congress:

ECR 2023

Poster Number:

C-20015

Type:

Scientific Exhibit

Keywords:

Artificial Intelligence, CT, Computer Applications-Detection, diagnosis, Segmentation, Staging, Cancer

Authors:

A. Corsi, F. Belmans, F. Blistein, S. Ahmed Ali, W. Vos, S. Sironi, M. Occhipinti

DOI:

10.26044/ecr2023/C-20015

DOI-Link:

https://dx.doi.org/10.26044/ecr2023/C-20015

References

Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Trans Med Imaging (2016) 35:1285–1298.
Tekchandani H, Verma S, Londhe N. Performance improvement of mediastinal lymph node severity detection using GAN and Inception network. Comput Methods Programs Biomed (2020) 194:105478. doi:https://doi.org/10.1016/j.cmpb.2020.105478
Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S, Liu J, Turkbey E, Summers RM. A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. in, eds. P. Golland, N. Hata, C. Barillot, J. Hornegger, R. Howe (Cham: Springer International Publishing), 520–527.

Fig 1: Comparison of axial slice with segmented lymph nodes in the original dataset...

Fig 2: Violin plots for the distribution of DICE score on the 5-fold cross model...

Fig 3: Comparison of axial slices with segmented lymph nodes with the model trained on...