Method, device, and storage medium for weakly-supervised universal lesion segmentation with regional level set loss

A segmentation device for medical images, comprising:

• a memory, containing a computer program stored thereon;
• and
• a processor, coupled with the memory and configured, when the computer program being executed, to perform a method including:
• 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.

A non-transitory computer-readable storage medium, storing program instructions configured to be executable by a computer to cause the computer to implement operations 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.