Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen

• Published in: Tomography (Ann Arbor) Vol. 7; no. 4; pp. 950 - 960
• Main Authors: Meddeb, Aymen, Kossen, Tabea, Bressem, Keno K, Hamm, Bernd, Nagel, Sebastian N
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 13.12.2021; MDPI AG
• Subjects: Algorithms; automated segmentation; Deep Learning; diagnosis; diagnostic techniques and procedures; Humans; image processing; Spleen - diagnostic imaging; diagnostic techniques and procedures; image processing; deep learning; diagnosis; automated segmentation
• ISSN: 2379-139X; 2379-1381; 2379-139X
• DOI: 10.3390/tomography7040078

The aim of this study was to develop a deep learning-based algorithm for fully automated spleen segmentation using CT images and to evaluate the performance in conditions directly or indirectly affecting the spleen (e.g., splenomegaly, ascites). For this, a 3D U-Net was trained on an in-house dataset (n = 61) including diseases with and without splenic involvement (in-house U-Net), and an open-source dataset from the Medical Segmentation Decathlon (open dataset, n = 61) without splenic abnormalities (open U-Net). Both datasets were split into a training (n = 32.52%), a validation (n = 9.15%) and a testing dataset (n = 20.33%). The segmentation performances of the two models were measured using four established metrics, including the Dice Similarity Coefficient (DSC). On the open test dataset, the in-house and open U-Net achieved a mean DSC of 0.906 and 0.897 respectively ( = 0.526). On the in-house test dataset, the in-house U-Net achieved a mean DSC of 0.941, whereas the open U-Net obtained a mean DSC of 0.648 ( < 0.001), showing very poor segmentation results in patients with abnormalities in or surrounding the spleen. Thus, for reliable, fully automated spleen segmentation in clinical routine, the training dataset of a deep learning-based algorithm should include conditions that directly or indirectly affect the spleen.