A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset

Shadab Ahamed; Yixi Xu; Ingrid Bloise; Joo H. O.; Carlos F. Uribe; Rahul Dodhia; Juan Lavista Ferres; Arman Rahmim

doi:10.1117/12.2652947

3 April 2023 A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset

Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O., Carlos F. Uribe, Rahul Dodhia, Juan Lavista Ferres, Arman Rahmim

Author Affiliations +

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124641Q (2023) https://doi.org/10.1117/12.2652947
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a higher probability of containing tumors, thereby directing physicians’ attention to the important slices. In this work, we train a ResNet-18 network to classify axial slices of lymphoma PET/CT images (collected from two institutions) depending on whether the slice intercepted a tumor (positive slice) in the 3D image or if the slice did not (negative slice). 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). Model performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC), and various binary classification metrics. We observe and describe a performance overestimation in the case of slice-level split as compared to the patient-level split training. The model trained using patient-level split data with the network input containing only PET slices in the CAG training regime was the best performing/generalizing model on a majority of metrics. Our models were additionally more closely compared using the sensitivity metric on the positive slices from their respective test sets.

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Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O., Carlos F. Uribe, Rahul Dodhia, Juan Lavista Ferres, and Arman Rahmim "A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641Q (3 April 2023); https://doi.org/10.1117/12.2652947

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