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Background: Ultrasound is the most popular tool for early detection of breast cancer because of its non radiation and low cost. However, breast ultrasound(BUS) images have low resolution and speckle noise, which make lesion segmentation become a challenge. Most of deep learning(DL) models applied on images segmentation don't have good generalization ability for BUS images. Therefore, it is time to go back to the classical method and consider combining it with DL to achieve more accurate and efficient effect in a semi-automatic way.
Methods: This paper mainly proposed an effective and efficient semi-automatic BUS images segmentation method, Adaptive morphological snake and marked watershed( AMSMW). It includes two parts: preprocessing and segmentation. In the first part, we combine contrast limited adaptive histogram equalization(CLAHE) and side window filtering(SWF) methods for the first time. In the second part, We use the proposed adaptive morphological snake algorithm (AMS) to provide a mark for marked watershed(MW) method.
Results: we tested on 500 BUS images, whose ratio of benign and malignant is 1:1. After quantitative and qualitative analysis, AMSMW is proven to outperform existing classical methods on the effectiveness and efficiency. Furthermore, we compared with Zhuang’s RDAU-NET on both our dataset and theirs. Experimental result showes AMSMW achieved better performance on most of indicators, including loss, accuracy, sensitivity, dice and F1-score.
Conlusions: The new image preprocessing method proposed by us has obvious effect on segmentation of breast ultrasound image. In addition, the proposed adaptive morphology snake method and optimized marked watershed turn out to be more efficient and effective than some relative classical method and the advanced DL method at present. Moreover, by studying on the algorithm’s sensitivity in segmenting benign and malignant tumors, we found that AMSMW is more sensitivity to malignant tumors, and more stable to benign tumors, which is significant for further research of precision medicine.
Keywords
Breast, morphology snake, segmentation, side window, Ultrasound, watershed
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On 31 Jan, 2021
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On 29 Jan, 2021
Editor assigned
On 28 Jan, 2021
Reviewer #1 agreed
On 28 Jan, 2021
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On 28 Jan, 2021
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On 28 Jan, 2021
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Review #6 received
Received 04 Feb, 2021
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Received 03 Feb, 2021
Review #3 received
Received 01 Feb, 2021
Review #5 received
Received 01 Feb, 2021
Review #2 received
Received 01 Feb, 2021
Reviewer #5 agreed
On 31 Jan, 2021
Reviewer #6 agreed
On 31 Jan, 2021
Reviewer #7 agreed
On 31 Jan, 2021
Reviewer #4 agreed
On 31 Jan, 2021
Reviewer #3 agreed
On 29 Jan, 2021
Reviewer #2 agreed
On 29 Jan, 2021
Editor assigned
On 28 Jan, 2021
Reviewers invited
Invitations sent on 28 Jan, 2021
Reviewer #1 agreed
On 28 Jan, 2021
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On 28 Jan, 2021
Editor invited
On 28 Jan, 2021
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This preprint is under consideration at BioMedical Engineering OnLine. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Badges
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Complete author information
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Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
The most recent version of this article is available here.
Version (no preprint)
Posted 22 May, 2021
Review #1 received
Received 20 May, 2021
Reviewer #2 agreed
On 19 May, 2021
Reviewers invited
Invitations sent on 19 May, 2021
Reviewer #1 agreed
On 19 May, 2021
Review #2 received
Received 19 May, 2021
Editor assigned
On 09 May, 2021
Submission checks complete
On 09 May, 2021
Editor invited
On 09 May, 2021
This version was not preprinted
Version (no preprint)
Posted 03 Apr, 2021
Review #4 received
Received 03 Apr, 2021
Reviewer #4 agreed
On 31 Mar, 2021
Reviewer #2 agreed
On 30 Mar, 2021
Reviewer #3 agreed
On 30 Mar, 2021
Reviewers invited
Invitations sent on 30 Mar, 2021
Reviewer #1 agreed
On 30 Mar, 2021
Review #1 received
Received 30 Mar, 2021
Review #2 received
Received 30 Mar, 2021
Editor assigned
On 27 Mar, 2021
Submission checks complete
On 27 Mar, 2021
Editor invited
On 27 Mar, 2021
This version was not preprinted
Version (no preprint)
Posted 27 Feb, 2021
Editorial decision: Major revision
On 27 Feb, 2021
Review #7 received
Received 13 Feb, 2021
Reviewer #7 agreed
On 06 Feb, 2021
Review #6 received
Received 04 Feb, 2021
Review #5 received
Received 03 Feb, 2021
Review #1 received
Received 01 Feb, 2021
Review #2 received
Received 01 Feb, 2021
Review #4 received
Received 01 Feb, 2021
Review #3 received
Received 01 Feb, 2021
Reviewer #5 agreed
On 31 Jan, 2021
Reviewer #6 agreed
On 31 Jan, 2021
Reviewer #4 agreed
On 31 Jan, 2021
Reviewers invited
Invitations sent on 31 Jan, 2021
Reviews received
Received 31 Jan, 2021
Reviewer #3 agreed
On 29 Jan, 2021
Reviewer #2 agreed
On 29 Jan, 2021
Editor assigned
On 28 Jan, 2021
Reviewer #1 agreed
On 28 Jan, 2021
Submission checks complete
On 28 Jan, 2021
Editor invited
On 28 Jan, 2021
First submitted
On 27 Jan, 2021
This version was not preprinted
No community comments so far
Review #6 received
Received 04 Feb, 2021
Review #4 received
Received 03 Feb, 2021
Review #3 received
Received 01 Feb, 2021
Review #5 received
Received 01 Feb, 2021
Review #2 received
Received 01 Feb, 2021
Reviewer #5 agreed
On 31 Jan, 2021
Reviewer #6 agreed
On 31 Jan, 2021
Reviewer #7 agreed
On 31 Jan, 2021
Reviewer #4 agreed
On 31 Jan, 2021
Reviewer #3 agreed
On 29 Jan, 2021
Reviewer #2 agreed
On 29 Jan, 2021
Editor assigned
On 28 Jan, 2021
Reviewers invited
Invitations sent on 28 Jan, 2021
Reviewer #1 agreed
On 28 Jan, 2021
Submission checks complete
On 28 Jan, 2021
Editor invited
On 28 Jan, 2021
Version 1
Posted 31 Dec, 2020
No comments provided
First submitted
On 27 Dec, 2020
Editor assigned
On 27 Dec, 2020
Submission checks complete
On 27 Dec, 2020
Editor invited
On 27 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The most recent version of this article is available here.
Background: Ultrasound is the most popular tool for early detection of breast cancer because of its non radiation and low cost. However, breast ultrasound(BUS) images have low resolution and speckle noise, which make lesion segmentation become a challenge. Most of deep learning(DL) models applied on images segmentation don't have good generalization ability for BUS images. Therefore, it is time to go back to the classical method and consider combining it with DL to achieve more accurate and efficient effect in a semi-automatic way.
Methods: This paper mainly proposed an effective and efficient semi-automatic BUS images segmentation method, Adaptive morphological snake and marked watershed( AMSMW). It includes two parts: preprocessing and segmentation. In the first part, we combine contrast limited adaptive histogram equalization(CLAHE) and side window filtering(SWF) methods for the first time. In the second part, We use the proposed adaptive morphological snake algorithm (AMS) to provide a mark for marked watershed(MW) method.
Results: we tested on 500 BUS images, whose ratio of benign and malignant is 1:1. After quantitative and qualitative analysis, AMSMW is proven to outperform existing classical methods on the effectiveness and efficiency. Furthermore, we compared with Zhuang’s RDAU-NET on both our dataset and theirs. Experimental result showes AMSMW achieved better performance on most of indicators, including loss, accuracy, sensitivity, dice and F1-score.
Conlusions: The new image preprocessing method proposed by us has obvious effect on segmentation of breast ultrasound image. In addition, the proposed adaptive morphology snake method and optimized marked watershed turn out to be more efficient and effective than some relative classical method and the advanced DL method at present. Moreover, by studying on the algorithm’s sensitivity in segmenting benign and malignant tumors, we found that AMSMW is more sensitivity to malignant tumors, and more stable to benign tumors, which is significant for further research of precision medicine.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
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