Office Action 1: Non-Final Rejection (2024-03-14)
Rejection types: 103 - Obviousness, Double Patenting

CLAIMS:
CLAIMS

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;
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.

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 determining 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 determining 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 determining 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 25

section type is distinguished with another section type; anddetermining the section type of the specified section imaged based on the extracted section type characteristics.

9\. The method of claim 1, wherein the determined section type comprises 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 comprise 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;
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 is acquired;
identify at least one measurement item which is measurable in the specified section image

according to the section type of the specified section image; andobtain 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 imaged based on the extracted section type characteristics.

17\. The apparatus of claim 11, wherein the determined section type comprises 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 comprise a shape, a brightness range or a size define by the gray values of the pixels.

OFFICE ACTION:
DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA  as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of U.S. Patent No. 11,717,183. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim an ultrasound system for automated identification of a measurement item.
U. S. Application No. 18/211,516
U. S. Patent No. 11,717,183
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; 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.
9. The method of claim 1, wherein the determined section type comprises 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.
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 determining 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 determining 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 determining 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 imaged based on the extracted section type characteristics.
10. The method of claim 1, wherein the one or more characteristics defined by the gray values of the pixels comprise 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; 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 is acquired; 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.
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.
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 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, 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; obtaining a correspondence between the section type and one or more measurement items which are measurable in an image of the section type; automatically identifying at least one measurement item which is measurable in the specified section image according to the correspondence and 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 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 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 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 imaged based on the extracted section type characteristics.
9. 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.
10. 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 identifying at least one measurement item which is measurable in the specified section image; determining an object corresponding to the identified at least one measurement item in the specified section image, wherein determining the object corresponding to the identified at least one measurement item in the specified section image comprises: automatically determining the object corresponding to the identified at least one measurement item in the specified section image; or detecting a trace operation of an operator on the specified section image, and determining the object corresponding to the identified at least one measurement item in the specified section image according to the detected trace operation; and obtaining a value of the identified at least one measurement item by performing a measurement on the determined object.
11. The method of claim 10, wherein automatically identifying the at least one measurement item which is measurable in the specified section image 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 outputting a measurement item corresponding to the training sample searched out as the at least one measurement item which is measurable in the specified section image.
13. The method of claim 10, 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.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-24 of U.S. Patent No. 10,898,109. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim an ultrasound system for automated identification of a measurement item.
U. S. Application No. 18/211,516
U. S. Patent No. 10,898,109
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; 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.
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 determining 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 determining 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.
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 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; obtaining a correspondence between the section type and one or more measurement items which are measurable in an image of the section type; automatically identifying at least one measurement item which is measurable in the specified section image according to the correspondence and the section type of the specified section image; detecting an object corresponding to the identified at least one measurement item in the specified section image; and obtaining a value of the identified at least one measurement item by performing a measurement on the detected object.
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 mode.
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 a section type of the specified section image based on 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 mode, respectively; and searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining section type of the training sample searched out as the section type of the specified section image.
7. The method of claim 3, wherein automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels comprises: extracting high intensity portions 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 portions based on the measuring mode to determine the section type of the specified section image.
10. The method of claim 1, wherein obtaining a value of the identified at least one measurement item comprises obtaining a value of the identified at least one measurement item manually, semi-automatically or automatically.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over U. S. Patent No. 5,588,435 to Weng et al. in view of U. S. Publication No. 2002/0102023 to Yamauchi.
Regarding Claims 1 and 11, Weng teaches a system and 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 (abstract; figs. 2, 3, and 6-8 acquiring gray scale images using ultrasound); identifying at least one measurement item corresponding to the specified section image based on the gray values of the pixels (abstract; figs. 2, 3, 6-8 and col. 4 lines 18-22, col. 12 lines 35-40 and col. 14 lines 4-10; furthermore col. 5 lines 34-47 teaches a fully automated system without user input); and measuring a measurement parameter of the specified section image based on the measurement item identified (abstract; col. 5 lines 34-47 ; figs. 9, 10 and claim 1 teaches measuring a parameter of the specified section image).
Weng does not expressly teach automatically 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 was acquired; using the automatically-determined section type to automatically identify at least one measurement item.
Yamauchi teaches automatically 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 was acquired (fig. 1 element 120 is an automatic contour extracting unit to detect contour of the section); using the automatically-determined section type to automatically identify at least one measurement item (fir. 1 element 115 is an automatic capacity measuring unit for detecting/measuring the capacity of the detected section marked by the contour).
It would be obvious to one of ordinary skill in the art at the time of filing to modify Weng, with a setup of automatically identifying a particular section of an area of a tissue, for automatically measuring an item, in the identified section, as taught by Yamauchi, since such a setup would result in faster location of the target region, and faster measurement of the target region.
Regarding Claims 2 and 12, Weng teaches that the measurement item is identified based on a comparative analysis of the gray values of the pixels with a preset data mode (figs. 2-6 and col. 13 lines 1-6 teaches analysis of gray value of the pixels).
Regarding Claims 3 and 13, Weng teaches acquiring a measuring mode used during tissue examination (col. 2 lines 20-40 teaches a measuring mode).
Regarding Claim 4, Weng teaches identifying at least one measurement item corresponding to the specified section image based on 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 (abstract; figs. 2, 3, 6-8 and col. 4 lines 18-22, col. 12 lines 35-40 and col. 14 lines 4-10); comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample mode, respectively (col. 13 lines 1-5); and searching a training sample whose characteristic is most similar to the characteristic of the specified section image and outputting measurement items corresponding to the training sample searched out as measurement items corresponding to the specified section image (col. 9 lines 45-50, col. 11 lines 1-10, col. 12 lines 25-27).
Regarding Claims 5, 8, 15, and 16, Weng teaches identifying at least one measurement item corresponding to the specified section image based on the gray values of the pixels comprises: extracting high intensity portions from the specified section image based on the gray values of the pixels of the specified section image; performing an identification on the high intensity portions based on the measuring mode to determine a section type of the specified section image; and determining a measurement item corresponding to the specified section image based on the section type determined and a measurement item corresponding to the section type determined (abstract; figs. 2, 3, 6-8 and col. 4 lines 18-22, col. 12 lines 35-40 and col. 14 lines 4-10).
Regarding Claims 6 and 14, Weng teaches measuring a measurement parameter of the specified section image based on the measurement item identified comprises measuring the measurement parameter of the specified section image based on the measurement item identified manually, semi-automatically or automatically (abstract; col. 5 lines 34-47, figs. 9, 10 and claim 1 teaches measuring the measurement parameter of the specified image).
Regarding claim 7,  Yamauchi teaches that 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 (Figs. 22-25 teaches automatic contour detection of the section/target region, furthermore it shows an ultrasound image, with the cone point to the transducer from the which the ultrasonic beam is generated, as such the direction of the image acquired).
Regarding Claims 9 and 17, Weng teaches that the determined section type comprises 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 (col. 8 lines 48-58 teaches abdominal circumference and head circumference).
Regarding Claims 10 and 18, Weng teaches that the one or more characteristics defined by the gray values of the pixels comprise a shape, a brightness range or a size define by the gray values of the pixels (col.6 lines 10-20 teaches gray scale values of pixels comprise shapes of tissue).
Conclusion


NEXT OFFICE ACTION: DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA  as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of U.S. Patent No. 11,717,183. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim an ultrasound system for automated identification of a measurement item.
U. S. Application No. 18/211,516
U. S. Patent No. 11,717,183
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 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; 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.
9. The method of claim 1, wherein the determined section type comprises 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.
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 determining 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 determining 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 determining 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 imaged based on the extracted section type characteristics.
10. The method of claim 1, wherein the one or more characteristics defined by the gray values of the pixels comprise 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; 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 is acquired; 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.
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.
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 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, 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; obtaining a correspondence between the section type and one or more measurement items which are measurable in an image of the section type; automatically identifying at least one measurement item which is measurable in the specified section image according to the correspondence and 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 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 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 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 imaged based on the extracted section type characteristics.
9. 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.
10. 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 identifying at least one measurement item which is measurable in the specified section image; determining an object corresponding to the identified at least one measurement item in the specified section image, wherein determining the object corresponding to the identified at least one measurement item in the specified section image comprises: automatically determining the object corresponding to the identified at least one measurement item in the specified section image; or detecting a trace operation of an operator on the specified section image, and determining the object corresponding to the identified at least one measurement item in the specified section image according to the detected trace operation; and obtaining a value of the identified at least one measurement item by performing a measurement on the determined object.
11. The method of claim 10, wherein automatically identifying the at least one measurement item which is measurable in the specified section image 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 outputting a measurement item corresponding to the training sample searched out as the at least one measurement item which is measurable in the specified section image.
13. The method of claim 10, 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.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-24 of U.S. Patent No. 10,898,109. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim an ultrasound system for automated identification of a measurement item.
U. S. Application No. 18/211,516
U. S. Patent No. 10,898,109
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 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; 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 determining 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 determining 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.
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 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; obtaining a correspondence between the section type and one or more measurement items which are measurable in an image of the section type; automatically identifying at least one measurement item which is measurable in the specified section image according to the correspondence and the section type of the specified section image; detecting an object corresponding to the identified at least one measurement item in the specified section image; and obtaining a value of the identified at least one measurement item by performing a measurement on the detected object.
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 mode.
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 a section type of the specified section image based on 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 mode, respectively; and searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining section type of the training sample searched out as the section type of the specified section image.
7. The method of claim 3, wherein automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels comprises: extracting high intensity portions 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 portions based on the measuring mode to determine the section type of the specified section image.
10. The method of claim 1, wherein obtaining a value of the identified at least one measurement item comprises obtaining a value of the identified at least one measurement item manually, semi-automatically or automatically.
Response to Arguments
Applicant’s arguments, see Remarks, filed 06/14/24, with respect to 103 rejection of claims 1-18 have been fully considered and are persuasive.  The 103 rejection of claims 1-18 has been withdrawn.
The double patenting rejection is maintained and is made FINAL.
Conclusion
THIS ACTION IS MADE FINAL.  Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action.  In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action.  In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.

CITED REFERENCE SUMMARIES: Ref US20060064016A1: Claims:
A method for automatic examination of echographic images of a cardiovascular system, in particular, for computing functionality indexes of a segment of the cardiovascular system, comprising the steps of: obtaining a succession of echographic images of said segment, said images being detected at constant intervals in a predetermined period, in order to provide a local sampling of a cardiovascular activity in said segment; creating at least one function of time, so-called “reference signal”, by measuring at least one scalar quantity on all the images of said succession and reporting a course of said quantity versus time; using this signal for computing said functionality indexes.
obtaining a succession of echographic images of said segment, said images being detected at constant intervals in a predetermined period, in order to provide a local sampling of a cardiovascular activity in said segment;
creating at least one function of time, so-called “reference signal”, by measuring at least one scalar quantity on all the images of said succession and reporting a course of said quantity versus time;
using this signal for computing said functionality indexes.
A method, according to claim 1 , wherein the further steps are provided of: choosing at least one conspicuous point of the morphology of said or each reference signal and obtaining a local synchronism signal at each repetition of the conspicuous point of the signal; sub-sampling said succession of echographic images by said local synchronism signal; providing an output of only the images relative to said sub-sampling, for measuring cardiovascular functionality indexes in said segment only at instants defined by said synchronism signal.
choosing at least one conspicuous point of the morphology of said or each reference signal and obtaining a local synchronism signal at each repetition of the conspicuous point of the signal;
sub-sampling said succession of echographic images by said local synchronism signal;
providing an output of only the images relative to said sub-sampling, for measuring cardiovascular functionality indexes in said segment only at instants defined by said synchronism signal.
Method, according to claim 1 , wherein said reference signal is a signal of comparison for determining a correct orientation of an echographic probe at the end of said step of determining said functionality indexes.
Method, according to claim 1 , wherein a step is provided of creating a graph of measured cardiovascular functionality indexes in the presence of transient situations provided by external mechanical, pharmacological or thermal stimuli.
Method, according to claim 1 , characterized in that said scalar quantity is obtained with edge detection techniques and/or contour tracking techniques, starting from echographic images of a type selected from the group comprised of: B-mode and color-Doppler.
Method, according to claim 1 , characterized in that said scalar quantity is selected from the group comprised of: measuring a diameter of a vessel; measuring an area of a cross section of a cardiovascular structure examined; an average of the grey levels or of color levels calculated on a reference window, or in any case a desired scalar or vectorial quantity determinable starting from the images of the succession.
measuring a diameter of a vessel;
measuring an area of a cross section of a cardiovascular structure examined;
an average of the grey levels or of color levels calculated on a reference window, or in any case a desired scalar or vectorial quantity determinable starting from the images of the succession.
Method, according to claim 2 , characterized in that said conspicuous points of the morphology of the reference signals are selected from the group comprised of: local maximum, local minimum, flexion points or other desired conspicuous point detectable by applying mathematical operators, among which the first derivative and the second derivative.
Method, according to claim 1 , characterized in that the step of measuring said cardiovascular functionality index is selected from the group comprised of: measuring the diameter of a vessel; measuring the area of a cross section of a cardiovascular structure examined; an average of the grey levels or of color levels calculated on a reference window, or in any case a desired scalar quantity determinable starting from the images of the succession defined by said synchronism signal.
measuring the diameter of a vessel;
measuring the area of a cross section of a cardiovascular structure examined;
an average of the grey levels or of color levels calculated on a reference window, or in any case a desired scalar quantity determinable starting from the images of the succession defined by said synchronism signal.
Method, according to claim 1 , wherein said cardiovascular functionality indexes are calculated on all the input images of the succession and then sub-sampled by said local synchronism signal.
Method, according to claim 1 , wherein said measurements of the cardiovascular functionality indexes are displayed with the succession of the starting images.
Method, according to claim 2 , wherein said measurements of the cardiovascular functionality indexes are displayed with the reference signal and the synchronism signal.
Method for automatic examination of a cardiovascular functionality by echographic imaging, characterized in that it comprises the steps of: prearranging a first image where a first edge has been defined, so-called “edge of reference” and a second edge, so-called “remaining part of the edge”, of a cardiovascular structure; applying on a second image successive to the first an estimated edge in a position exactly corresponding to said edge of reference of the first image; and wherein on said second image the following steps are carried out: creating a first approximated edge which is distant a fixed amount from said estimated edge; computing an edge of reference by an “edge detection” algorithm using points belonging to said first approximated edge; from said edge of reference, computing a remaining part of the edge estimated using data on the geometry of the examined calculated on images previous to said image; computing a second approximated edge distant a fixed amount from said estimated remaining part of the edge; computing a remaining part of the edge estimated by an “edge detection” algorithm using points belonging to said second approximated edge.
prearranging a first image where a first edge has been defined, so-called “edge of reference” and a second edge, so-called “remaining part of the edge”, of a cardiovascular structure;
applying on a second image successive to the first an estimated edge in a position exactly corresponding to said edge of reference of the first image; and wherein on said second image the following steps are carried out:
and wherein on said second image the following steps are carried out:
creating a first approximated edge which is distant a fixed amount from said estimated edge;
computing an edge of reference by an “edge detection” algorithm using points belonging to said first approximated edge;
from said edge of reference, computing a remaining part of the edge estimated using data on the geometry of the examined calculated on images previous to said image;
computing a second approximated edge distant a fixed amount from said estimated remaining part of the edge;
computing a remaining part of the edge estimated by an “edge detection” algorithm using points belonging to said second approximated edge.
Method, according to claim 12 , characterized in that said positive amount is chosen so that said first and second approximated edge are each a set of points in said cardiovascular structure.
Method, according to claim 12 , characterized in that said edge of reference is a lower edge of a longitudinal cross section of a vessel, i.e. the edge where an echo signal transmitted by a probe encounters first blood and then tissue.
Method, according to claim 12 , wherein said edge detection operator is a mathematical operator suitable for determining an edge starting from a point near this edge, selected from the group comprised of: the operator of Canny, the Laplacian of Gaussian, the barycentre of dispersion.
Method, according to claim 12 , whereby the echographic probe is located so that said edge of reference is substantially orthogonal to the direction of propagation of the ultrasonic signal.
Apparatus for measuring cardiovascular functionality indexes on sequences of ultrasonic pulse images comprising: a first processor that computes all the images of the succession and generates reference signals and a local synchronism signal; a sub-sampler of images that extracts from the succession of ultrasonic images that correspond to synchronism instants; a second processor that computes sub-sampled images and extracts measurements of cardiovascular functionality indexes.
a first processor that computes all the images of the succession and generates reference signals and a local synchronism signal;
a sub-sampler of images that extracts from the succession of ultrasonic images that correspond to synchronism instants;
a second processor that computes sub-sampled images and extracts measurements of cardiovascular functionality indexes.
Apparatus, according to claim 17 , comprising a delay between the succession of input images provided to the first processor and the succession of input images provided to the sub-sampler.
Ref US20180085043A1: Claims:
A method comprising: obtaining a set of frames of 2D ultrasound images; using one or more processors to: develop a prospect model indicating a likelihood that frames within the set include an organ of interest (OOI); identify primary and secondary reference frames from the set of the frames based on the prospect model; determine a characteristic of interest in the primary reference frame; select a candidate shape for the OOI based on the character of interest in the primary reference frame; and adjust the candidate shape based on the secondary reference frames to form a resultant shape for the OOI.
obtaining a set of frames of 2D ultrasound images;
using one or more processors to: develop a prospect model indicating a likelihood that frames within the set include an organ of interest (OOI); identify primary and secondary reference frames from the set of the frames based on the prospect model; determine a characteristic of interest in the primary reference frame; select a candidate shape for the OOI based on the character of interest in the primary reference frame; and adjust the candidate shape based on the secondary reference frames to form a resultant shape for the OOI.
develop a prospect model indicating a likelihood that frames within the set include an organ of interest (OOI);
identify primary and secondary reference frames from the set of the frames based on the prospect model;
determine a characteristic of interest in the primary reference frame;
select a candidate shape for the OOI based on the character of interest in the primary reference frame; and
adjust the candidate shape based on the secondary reference frames to form a resultant shape for the OOI.
The method of claim 1 , wherein the identify operation includes identifying, from the set of frames, an ultrasound image that intersects a center of the OOI, the ultrasound image representing the primary reference frame.
The method of claim 1 , wherein the identify operation includes identifying, from the set of frames, ultrasound images intersects intermediate and peripheral edges of the OOI, the ultrasound images representing the primary and secondary reference frames.
The method of claim 1 , wherein the develop operation includes identifying a pixel pattern between adjacent frames in the set of frames and deriving the prospect model based on the pixel pattern.
The method of claim 1 , wherein the develop operation includes identifying regions of interest in adjacent frames in the set of frames, calculating pixel intensity information for the regions of interest, and deriving the prospect model based on the pixel intensity information.
The method of claim 1 , wherein the develop operation includes determining tilt angle information regarding for at least a portion of the set of frames, and deriving the prospect model based on the tilt angle information.
The method of claim 1 , wherein the characteristic of interest represents at least one of a boundary of the 001 or a ratio of dimensions for the OOI.
The method of claim 1 , wherein the adjust operation includes adjusting at least one of a size or contour of the candidate shape based on characteristics of interest in the secondary reference frames.
The method of claim 1 , further comprising calculating a volume for the resultant shape.
The method of claim 1 , wherein the OOI is a bladder, a stomach, a kidney or a liver.
A mobile ultrasound imaging system comprising: a portable host system having one or more processors and a memory for storing a plurality of applications that include corresponding programmed instructions, wherein when a select application is activated the one or more processors execute programmed instructions of the select application by performing the following operations: obtain a set of frames of 2D ultrasound images; develop a prospect model indicating a likelihood that frames within the set include an organ of interest (OOI); identify primary and secondary reference frames from the set of the frames based on the prospect model; determine a characteristic of interest in the primary reference frame; select a candidate shape for the OOI based on the character of interest in the primary reference frame; and adjust the candidate shape based on the secondary reference frames to form a resultant shape for the OOI.
a portable host system having one or more processors and a memory for storing a plurality of applications that include corresponding programmed instructions, wherein when a select application is activated the one or more processors execute programmed instructions of the select application by performing the following operations: obtain a set of frames of 2D ultrasound images; develop a prospect model indicating a likelihood that frames within the set include an organ of interest (OOI); identify primary and secondary reference frames from the set of the frames based on the prospect model; determine a characteristic of interest in the primary reference frame; select a candidate shape for the OOI based on the character of interest in the primary reference frame; and adjust the candidate shape based on the secondary reference frames to form a resultant shape for the OOI.
obtain a set of frames of 2D ultrasound images;
The mobile ultrasound imaging system of claim 11 , wherein the identify operation includes identifying, from the set of frames, an ultrasound image that intersects a center of the OOI, the ultrasound image representing the primary reference frame.
The mobile ultrasound imaging system of claim 11 , wherein the identify operation includes identifying, from the set of frames, ultrasound images intersects intermediate and peripheral edges of the OOI, the ultrasound images representing the primary and secondary reference frames.
The mobile ultrasound imaging system of claim 11 , wherein the develop operation includes identifying a pixel pattern between adjacent frames in the set of frames and deriving the prospect model based on the pixel pattern.
The mobile ultrasound imaging system of claim 11 , wherein the develop operation includes identifying regions of interest in adjacent frames in the set of frames, calculating pixel intensity information for the regions of interest, and deriving the prospect model based on the pixel intensity information.
The mobile ultrasound imaging system of claim 11 , wherein the develop operation includes determining tilt angle information regarding for at least a portion of the set of frames, and deriving the prospect model based on the tilt angle information.
The mobile ultrasound imaging system of claim 11 , wherein the characteristic of interest represents at least one of a boundary of the OOI or a ratio of dimensions for the OOI.
The mobile ultrasound imaging system of claim 11 , wherein the adjust operation includes adjusting at least one of a size or contour of the candidate shape based on characteristics of interest in the secondary reference frames.
The mobile ultrasound imaging system of claim 11 , further comprising calculating a volume for the resultant shape.
A tangible and non-transitory computer readable medium comprising one or more programmed instructions configured to direct one or more processors to: obtain a set of frames of 2D ultrasound images; develop a prospect model indicating a likelihood that frames within the set include an organ of interest (OOI); identify primary and secondary reference frames from the set of the frames based on the prospect model; determine a characteristic of interest in the primary reference frame; select a candidate shape for the OOI based on the character of interest in the primary reference frame; and adjust the candidate shape based on the secondary reference frames to form a resultant shape for the OOI.
Ref US20200302596A1: Claims:
A method for estimating uncertainty measurements on lesion segmentation, the method comprising: acquiring, by a magnetic resonance imaging (MRI) device, MRI data for a patient; generating, using a first trained network, segmented lesion data from the MRI data; estimating, by an image processor, voxel level measures of uncertainty from the segmented lesion data; generating, by the image processor, lesion level measures of uncertainty from the voxel level measures of uncertainty; and predicting, by a second trained network, an image level uncertainty measure for the segmented lesion data, the second trained network configured to receive the MRI data, the segmented lesion data, the voxel level measures of uncertainty, and the lesion level measures of uncertainty as input data and to predict the image level uncertainty measure.
acquiring, by a magnetic resonance imaging (MRI) device, MRI data for a patient;
generating, using a first trained network, segmented lesion data from the MRI data;
estimating, by an image processor, voxel level measures of uncertainty from the segmented lesion data;
generating, by the image processor, lesion level measures of uncertainty from the voxel level measures of uncertainty; and
predicting, by a second trained network, an image level uncertainty measure for the segmented lesion data, the second trained network configured to receive the MRI data, the segmented lesion data, the voxel level measures of uncertainty, and the lesion level measures of uncertainty as input data and to predict the image level uncertainty measure.
The method of claim 1 , further comprising: increasing, by the image processor, information density of the segmented lesion data, the voxel level measures of uncertainty, and the lesion level measures of uncertainty.
increasing, by the image processor, information density of the segmented lesion data, the voxel level measures of uncertainty, and the lesion level measures of uncertainty.
The method of claim 2 , wherein increasing information density comprises: increasing, by the image processor, information density by Euclidean distance transform for the segmented lesion data; and increasing, by the image processor, information density by geodesic distance transform for the voxel level measures of uncertainty and the lesion level measures of uncertainty.
increasing, by the image processor, information density by Euclidean distance transform for the segmented lesion data; and
increasing, by the image processor, information density by geodesic distance transform for the voxel level measures of uncertainty and the lesion level measures of 
[Truncated to input limit]