{"id":10314,"date":"2022-09-26T15:18:00","date_gmt":"2022-09-26T13:18:00","guid":{"rendered":"https:\/\/sano.science\/?p=10314"},"modified":"2024-03-14T13:31:46","modified_gmt":"2024-03-14T12:31:46","slug":"sano-science-team-representation-in-singapore-at-miccai-2022","status":"publish","type":"post","link":"https:\/\/sano.science\/sano-science-team-representation-in-singapore-at-miccai-2022\/","title":{"rendered":"Sano Science team representation in Singapore at MICCAI 2022\u00a0"},"content":{"rendered":"\n

Szymon P\u0142otka<\/strong> and Micha\u0142 Grzeszczyk<\/strong> presented the paper entitled \u201eBabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video<\/strong>\u201d.\u00a0

As we can read in the abstract, predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery. Accurate prediction of weight using prenatal ultrasound is challenging as it requires images of specific fetal body parts during advanced pregnancy which is difficult to capture due to poor quality of images caused by the lack of amniotic fluid. As a consequence, predictions which rely on standard methods often suffer from significant errors. In this paper researchers propose the Residual Transformer Module which extends a 3D ResNet-based network for analysis of 2D + t spatio-temporal ultrasound video scans. Their end-to-end method, called BabyNet, automatically predicts fetal birth weight based on fetal ultrasound video scans. The BabyNet was evaluated by using a dedicated clinical set comprising 225 2D fetal ultrasound videos of pregnancies from 75 patients performed one day prior to delivery. Experimental results show that BabyNet outperforms several state-of-the-art methods and estimates the weight at birth with accuracy comparable to human experts. Furthermore, combining estimates provided by human experts with those computed by BabyNet yields the best results, outperforming either of other methods by a significant margin.\u00a0<\/p>\n\n\n

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Paper is available here LINK<\/a> & GitHub code<\/a>. 

Monika Pytlarz, Sylwia Malec, and Joan Falc\u00f3 Roget \u2013 team members of Brain and More Lab in Sano \u2013 with colleagues from the University of Pennsylvania were driving a BrainLes workshop on the first day of satellite events of MICCAI 2022. 

The event was held in conjunction with the following MICCAI Challenges: <\/strong><\/p>\n\n\n