P122: DEEP LEARNING-BASED ALGORITHM FOR AUTOMATIC SPLEEN LENGTH MEASUREMENT
Authors: Z Yuan, A King, B Inusa
Journal: HemaSphere (2022)

Background: In Sickle Cell Disease (SCD), spleen enlargement happens frequently in children. Acute splenic sequestration occurs when the red pulp of the spleen filtering action is exaggerated and the red blood cells are trapped, which causes the spleen sudden enlargement. If left untreated, this condition can be life-threatening. The spleen length is typically measured in the clinic, including manual palpation and following ultrasound examination. However, the common clinical workflow is non-quantitative (manual palpation) and suffers from inter- and intra- experts variability (ultrasound examination). In the developing area, where there is a high prevalence of SCD, there is often a lack of experienced sonographers to conduct the length measurement while ultrasound examination. To address this problem, we set out to use a deep learning-based strategy to develop a full pipeline, which consists of a quality control system and an automated length measurement for spleen assessment. Methods: We investigated the use of deep learning to automate the process of spleen length measurement. A deep learning model based on the ResNet was developed to classify whether the input ultrasound image could be used to measure the length. A U-net based network was trained to automatically segment the spleen. The principal components analysis was then applied to the segmentation to find the longest axis of the spleen, and the splenic length was computed based on the projection of the points within the segmentation to the axis. Results: 475 ultrasound images of the spleen were used in this study. 475 images were used to train the quality control network (420 good quality images and 55 bad quality images), and 420 images (good quality images) were used in developing the automated spleen length measurement system. These images were obtained from SCD patients and were acquired as part of routine examinations at the Evelina Children’s Hospital, London. 108 images were manually measured by three experienced sonographers to computer the inter-expert variability as a comparison to our methods. The mean percentage error of our length measurement has reached 4.58% on good quality images which was within the human expert error (5.78%). Our quality control system has reached 0.964 sensitivity and 0.636 specificity. After incorporating bad quality images and the quality control system, the full pipeline reached 4.88% percentage length error, which is still within the human expert variability. Conclusion: We have demonstrated that deep learning can be used for automating the length measurement for the spleen from ultrasound images. The performance of the developed pipeline has reached the human expert level. This can be potentially used in a real clinical scenario. References 1. Chakravorty, T.N. Williams, Sickle cell disease: A neglected chronic disease of increasing global health importance, Arch. Dis. Child. 100 (2015). https://doi.org/10.1136/archdischild-2013–303773. 2. B. Piel, A.P. Patil, R.E. Howes, O.A. Nyangiri, P.W. Gething, M. Dewi, W.H. Temperley, T.N. Williams, D.J. Weatherall, S.I. Hay, Global epidemiology of Sickle haemoglobin in neonates: A contemporary geostatistical model-based map and population estimates, Lancet. 381 (2013). https://doi.org/10.1016/S0140-6736(12)61229-X. 3. D. Grosse, I. Odame, H.K. Atrash, D.D. Amendah, F.B. Piel, T.N. Williams, Sickle cell disease in Africa: A neglected cause of early childhood mortality, Am. J. Prev. Med. 41 (2011). https://doi.org/10.1016/j.amepre.2011.09.013. 4. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016. https://doi.org/10.1109/CVPR.2016.90. 5. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 2015. https://doi.org/10.1007/978-3-319-24574-4_28.