Automated uncertainty estimation of lesion segmentation
US-2020302596-A1 · Sep 24, 2020 · US
US12336808B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12336808-B2 |
| Application number | US-202318211516-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jun 19, 2023 |
| Priority date | Mar 20, 2014 |
| Publication date | Jun 24, 2025 |
| Grant date | Jun 24, 2025 |
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A method for automatic identification of a measurement item includes: acquiring gray values of pixels of a specified section image, where the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image is acquired; identifying at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and obtaining a value of the identified at least one measurement item according to the specified section image.
Opening claim text (preview).
What is claimed is: 1. A method for automatic identification of a measurement item, comprising: acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; automatically determining, by a processor of an ultrasound imaging device, a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired; automatically identifying at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and obtaining a value of the identified at least one measurement item according to the specified section image. 2. The method of claim 1 , wherein the measurement item is identified based on a comparative analysis of the gray values of the pixels with a preset data model. 3. The method of claim 2 , further comprising acquiring a measuring mode used during tissue examination. 4. The method of claim 1 , wherein automatically determining, by the processor of the ultrasound imaging device, the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image; comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample model, respectively; and searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining a section type of the training sample searched out as the section type of the specified section image. 5. The method of claim 3 , wherein automatically determining, by the processor of the ultrasound imaging device, the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: extracting a high intensity portion from the specified section image based on the gray values of the pixels of the specified section image; and performing an identification on the high intensity portion based on the measuring mode to determine the section type of the specified section image. 6. The method of claim 1 , wherein obtaining the value of the identified at least one measurement item comprises obtaining the value of the identified at least one measurement item manually, semi-automatically or automatically. 7. The method of claim 1 , wherein the particular section of the section type corresponds to a direction along which a desired section image is acquired from the particular area of the tissue. 8. The method of claim 1 , wherein automatically determining, by the processor of the ultrasound imaging device, the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: extracting section type characteristics of the specified section image based on which one section type is distinguished with another section type; and determining the section type of the specified section image based on the extracted section type characteristics. 9. The method of claim 1 , wherein the determined section type is a head circumference section which contains a skull of a fetus, an abdominal circumference section which contains an abdomen of a fetus or a femur section which contains a thigh bone of a fetus. 10. The method of claim 1 , wherein the one or more characteristics defined by the gray values of the pixels are a shape, a brightness range or a size define by the gray values of the pixels. 11. An ultrasound imaging apparatus, comprising: a probe which transmits ultrasound waves to a tissue and receives ultrasound echoes; a signal processor which processes the ultrasound echoes to generate ultrasound image data; and an image processor which processes the ultrasound image data and generates section images; wherein the image processor is further configured to: acquire gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; automatically determine a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired; automatically identify at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and obtain a value of the identified at least one measurement item according to the specified section image. 12. The apparatus of claim 11 , wherein the image processor is further configured to identify the measurement item based on a comparative analysis of the gray values of the pixels with a preset data model. 13. The apparatus of claim 11 , wherein the image processor is further configured to: generate a characteristic of the specified section image based on the gray values of the pixels of the specified section image; compare the characteristic of the specified section image with characteristic of training samples in a preset training sample mode, respectively; and search a training sample whose characteristic is most similar to the characteristic of the specified section image and determine a section type of the training sample searched out as the section type of the specified section image. 14. The apparatus of claim 11 , wherein the image processor is further configured to obtain the value of the identified at least one measurement item manually, semi-automatically or automatically. 15. The apparatus of claim 11 , wherein the particular section of the section type corresponds to a direction along which a desired section image is acquired from the particular area of the tissue. 16. The apparatus of claim 11 , wherein the image processor is further configured to determine the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprising: extracting section type characteristics of the specified section image based on which one section type is distinguished with another section type; and determining the section type of the specified section image based on the extracted section type characteristics. 17. The apparatus of claim 11 , wherein the determined section type is a head circumference section which contains a skull of a fetus, an abdominal circumference section which contains an abdomen of a fetus or a femur section which contains a thigh bone of a fetus. 18. The apparatus of claim 11 , wherein the one or more characteristics defined by the gray values of the pixels are a shape, a brightness range or a size define by the gray values of the pixels.
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