![]() ![]() 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%). We also incorporated a quality control (QC) model to help less experienced sonographers ensure the quality of ultrasound images before measurement. The second approach is based on direct regression of spleen length. 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 segmen- tation. Methods: Two deep learning-based approaches were investigated to achieve automated spleen length measure- ment from ultrasound images. Our objective was to automate the spleen length measurement process. However, the current workflow is prone to intra- and inter-observer variability and is dependent on the experience of the sonographer. Therefore, spleen size assessments from ultrasound imaging are commonly performed in SCD clinics, and typically involve measuring the length of the spleen. 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). ![]()
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