no code implementations • 11 Mar 2024 • Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O, Carlos F. Uribe, Rahul Dodhia, Juan L. Ferres, Arman Rahmim
Various instances of the network were trained on 2D axial datasets created in different ways: (i) slice-level split and (ii) patient-level split; inputs of different types were used: (i) only PET slices and (ii) concatenated PET and CT slices; and different training strategies were employed: (i) center-aware (CAW) and (ii) center-agnostic (CAG).
no code implementations • 11 Mar 2024 • Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise, Claire Gowdy, Patrick Martineau, Don Wilson, Carlos F. Uribe, Arman Rahmim, Fereshteh Yousefirizi
Manual segmentation of tumors in whole-body PET images is time-consuming, labor-intensive and operator-dependent.
1 code implementation • 16 Nov 2023 • Shadab Ahamed, Yixi Xu, Claire Gowdy, Joo H. O, Ingrid Bloise, Don Wilson, Patrick Martineau, François Bénard, Fereshteh Yousefirizi, Rahul Dodhia, Juan M. Lavista, William B. Weeks, Carlos F. Uribe, Arman Rahmim
This study performs comprehensive evaluation of four neural network architectures (UNet, SegResNet, DynUNet, and SwinUNETR) for lymphoma lesion segmentation from PET/CT images.