US 11,900,596 B2
Method, device, and storage medium for weakly-supervised universal lesion segmentation with regional level set loss
Youbao Tang, Bethesda, MD (US); Jinzheng Cai, Bethesda, MD (US); Ke Yan, Bethesda, MD (US); and Le Lu, Bethesda, MD (US)
Assigned to PING AN TECHNOLOGY (SHENZHEN) CO., LTD., Shenzhen (CN)
Filed by Ping An Technology (Shenzhen) Co., Ltd., Shenzhen (CN)
Filed on Sep. 20, 2021, as Appl. No. 17/479,560.
Claims priority of provisional application 63/174,821, filed on Apr. 14, 2021.
Claims priority of provisional application 63/174,826, filed on Apr. 14, 2021.
Prior Publication US 2022/0351386 A1, Nov. 3, 2022
Int. Cl. G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 3/40 (2006.01); G06F 18/213 (2023.01); G06F 18/25 (2023.01)
CPC G06T 7/0012 (2013.01) [G06F 18/213 (2023.01); G06F 18/253 (2023.01); G06T 3/40 (2013.01); G06T 7/11 (2017.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/03 (2022.01)] 19 Claims
OG exemplary drawing
 
1. A lesion segmentation method for medical images, comprising:
inputting an image into an attention-enhanced high-resolution network (AHRNet) to extract feature maps at multiple scales;
generating a first feature map according to the extracted feature maps;
generating a first probability map according to the first feature map, concatenating the first probability map with the first feature map to form a concatenated first feature map, calculating a first segmentation loss based on the first probability map, and updating the AHRNet using the first segmentation loss;
generating a second feature map by up-sampling the concatenated first feature map using a deconvolutional layer, and scaling the second feature map to form a third feature map;
generating a second probability map according to the third feature map, concatenating the second probability map with the third feature map to form a concatenated third feature map, calculating a second segmentation loss based on the second probability map, and updating the AHRNet using the second segmentation loss;
generating a fourth feature map by up-sampling the concatenated third feature map using a deconvolutional layer, and scaling the fourth feature map to form a fifth feature map;
generating a third probability map according to the fifth feature map, and calculating a third segmentation loss and a regional level set loss based on the third probability map; and
updating the AHRNet using the third segmentation loss and the regional level set loss, and outputting the third probability map.