Deep learning-based quality-controlled spleen assessment from ultrasound images

Zhen Yuan*, Esther Puyol-Antón, Haran Jogeesvaran, Nicola Smith, Baba Inusa, Andrew P. King

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
25 Downloads (Pure)


Objective: Splenomegaly (abnormal splenic enlargement) is a potentially life-threatening condition that occurs in a range of clinical scenarios, including in patients suffering from Sickle cell disease (SCD). Therefore, spleen size assessments from ultrasound imaging are commonly performed in SCD clinics, and typically involve measuring the length of the spleen. However, the current workflow is prone to intra- and inter-observer variability and is dependent on the experience of the sonographer. Our objective was to automate the spleen length measurement process. Methods: Two deep learning-based approaches were investigated to achieve automated spleen length measurement from ultrasound images. One is a segmentation-based approach, where we trained a modified U-Net to obtain a spleen segmentation and then applied post-processing to measure the spleen length from the segmentation. The second approach is based on direct regression of spleen length. We also incorporated a quality control (QC) model to help less experienced sonographers ensure the quality of ultrasound images before measurement. Results: Our best model (segmentation-based approach) reached a mean percentage length error (MPLE) of 4.58% on good quality images, which is within the range of human expert inter-observer variability (5.78%). After including bad quality images, the incorporation of the QC step resulted in a significant reduction in MPLE (from 5.76% to 4.88%). Conclusion: Automated, quality-controlled spleen length measurement from ultrasound has been achieved with human-level accuracy. Significance: This proposed framework has the potential to assist in making robust and accurate assessments of the spleen, especially in settings where there is a lack of experienced sonographers.

Original languageEnglish
Article number103724
JournalBiomedical Signal Processing and Control
Early online date25 Apr 2022
Publication statusPublished - Jul 2022


  • Deep learning
  • Segmentation
  • Sickle cell disease
  • Spleen ultrasound image
  • Splenomegaly


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