Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation
This study developed a recursive training strategy to train a deep learning model for nuclei detection and segmentation using incomplete annotation. A dataset of 141 H&E stained breast cancer pathologic images with incomplete annotation was randomly split into training/validation set and...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9770035/ |
| _version_ | 1818008974976352256 |
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| author | Chuan Zhou Heang-Ping Chan Lubomir M. Hadjiiski Aamer Chughtai |
| author_facet | Chuan Zhou Heang-Ping Chan Lubomir M. Hadjiiski Aamer Chughtai |
| author_sort | Chuan Zhou |
| collection | DOAJ |
| description | This study developed a recursive training strategy to train a deep learning model for nuclei detection and segmentation using incomplete annotation. A dataset of 141 H&E stained breast cancer pathologic images with incomplete annotation was randomly split into training/validation set and test set of 89 and 52 images, respectively. The positive training samples were extracted at each annotated cell and augmented with affine translation. The negative training samples were selected from the non-cellular regions free of nuclei using a histogram-based semi-automatic method. A U-Net model was initially trained by minimizing a custom loss function. After the first stage of training, the trained U-Net model was applied to the images in the training set in an inference mode. The U-Net segmented objects with high quality were selected by a semi-automated method. Combining the newly selected high quality objects with the annotated nuclei and the previously generated negative samples, the U-Net model was retrained recursively until the stopping criteria were satisfied. For the 52 test images, the U-Net trained with and without using our recursive training method achieved a sensitivity of 90.3% and 85.3% for nuclei detection, respectively. For nuclei segmentation, the average Dice coefficient and average Jaccard index were 0.831±0.213 and 0.750±0.217, 0.780±0.270 and 0.697±0.264, for U-Net with and without recursive training, respectively. The improvement achieved by our proposed method was statistically significant (<inline-formula> <tex-math notation="LaTeX">$P < 0.05$ </tex-math></inline-formula>). In conclusion, our recursive training method effectively enlarged the set of annotated objects for training the deep learning model and further improved the detection and segmentation performance. |
| first_indexed | 2024-04-14T05:37:02Z |
| format | Article |
| id | doaj.art-e553f448c2ee454e85e2315af9ea0e49 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-14T05:37:02Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e553f448c2ee454e85e2315af9ea0e492022-12-22T02:09:37ZengIEEEIEEE Access2169-35362022-01-0110493374934610.1109/ACCESS.2022.31729589770035Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete AnnotationChuan Zhou0https://orcid.org/0000-0002-0609-1658Heang-Ping Chan1https://orcid.org/0000-0001-7777-9006Lubomir M. Hadjiiski2Aamer Chughtai3Department of Radiology, University of Michigan, Ann Arbor, MI, USADepartment of Radiology, University of Michigan, Ann Arbor, MI, USADepartment of Radiology, University of Michigan, Ann Arbor, MI, USADepartment of Radiology, University of Michigan, Ann Arbor, MI, USAThis study developed a recursive training strategy to train a deep learning model for nuclei detection and segmentation using incomplete annotation. A dataset of 141 H&E stained breast cancer pathologic images with incomplete annotation was randomly split into training/validation set and test set of 89 and 52 images, respectively. The positive training samples were extracted at each annotated cell and augmented with affine translation. The negative training samples were selected from the non-cellular regions free of nuclei using a histogram-based semi-automatic method. A U-Net model was initially trained by minimizing a custom loss function. After the first stage of training, the trained U-Net model was applied to the images in the training set in an inference mode. The U-Net segmented objects with high quality were selected by a semi-automated method. Combining the newly selected high quality objects with the annotated nuclei and the previously generated negative samples, the U-Net model was retrained recursively until the stopping criteria were satisfied. For the 52 test images, the U-Net trained with and without using our recursive training method achieved a sensitivity of 90.3% and 85.3% for nuclei detection, respectively. For nuclei segmentation, the average Dice coefficient and average Jaccard index were 0.831±0.213 and 0.750±0.217, 0.780±0.270 and 0.697±0.264, for U-Net with and without recursive training, respectively. The improvement achieved by our proposed method was statistically significant (<inline-formula> <tex-math notation="LaTeX">$P < 0.05$ </tex-math></inline-formula>). In conclusion, our recursive training method effectively enlarged the set of annotated objects for training the deep learning model and further improved the detection and segmentation performance.https://ieeexplore.ieee.org/document/9770035/Deep learningU-Net modelpathology imagesimage segmentation |
| spellingShingle | Chuan Zhou Heang-Ping Chan Lubomir M. Hadjiiski Aamer Chughtai Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation IEEE Access Deep learning U-Net model pathology images image segmentation |
| title | Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation |
| title_full | Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation |
| title_fullStr | Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation |
| title_full_unstemmed | Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation |
| title_short | Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation |
| title_sort | recursive training strategy for a deep learning network for segmentation of pathology nuclei with incomplete annotation |
| topic | Deep learning U-Net model pathology images image segmentation |
| url | https://ieeexplore.ieee.org/document/9770035/ |
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