Patent ID: 11972574
Assignee: DOTTER CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. An image segmentation device comprising:
a non-transient, computer-readable storage unit storing a segmentation model trained using learning images so as to segment at least one predetermined object; and
at least one processor inputting input data into the segmentation model and segmenting at least one predetermined object in the input data,
wherein the segmentation model includes
an encoder including at least one dimension reduction block reducing a dimension of the input data,
a decoder including at least one dimension increase block increasing the dimension of output data outputted from the encoder by using data outputted from at least one dimension reduction block, and
an auxiliary classification model receiving the output data outputted from the encoder and recognizing whether a specific object is included in the output data to produce a binary classification result,
wherein the at least one predetermined object includes at least one of a strut and tissue of a biodegradable stent, and
wherein the input data is an intravascular optical coherence tomography (OCT) image, and
wherein the processor optimizes weight values included in the decoder of the segmentation model so as to express each of the at least one predetermined object included in the learning images in a final feature value map of the decoder and wherein the processor optimizes weight values included in the encoder and the auxiliary classification model of the segmentation model so as to recognize whether the specific object is included in the learning images to produce the binary classification result through the auxiliary classification model so that a feature value for strut segmentation of the encoder and the decoder is further optimized when training the segmentation model, wherein further optimization of the encoder and the decoder comprises minimizing a loss function L that is calculated using a term for the binary classification result of the auxiliary classification model,
and
wherein the learning images include
a plurality of first learning images including a first biodegradable stent configured by a strut having a first thickness, and
a plurality of second learning images including a second biodegradable stent configured by a strut having a second thickness different from the first thickness.