Lesion Localization and Pathological Diagnosis of Ovine Pulmonary Adenocarcinoma Based on MASK R-CNN
by
, , ,
Sixu Chen
1,2,3,
Pei Zhang
1,2,3,
Xujie Duan
1,2,3,
Anyu Bao
1,2,3,
Buyu Wang
4,
Yufei Zhang
1,2,3
,
Huiping Li
1,2,3,
Liang Zhang
1,2,3 and
Shuying Liu
1,2,3,* 1
College of Veterinary Medicine, Inner Mongolia Agricultural University, Zhao Wu Da Road No. 306, Hohhot 010018, China
2
Inner Mongolia Key Laboratory of Basic Veterinary Science, Hohhot 010018, China
3
Key Laboratory of Clinical Diagnosis and Treatment Technology in Animal Disease, Ministry of Agriculture, Hohhot 010018, China
4
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010010, China
*
Author to whom correspondence should be addressed.
Animals 2024, 14(17), 2488; https://doi.org/10.3390/ani14172488
Submission received: 26 July 2024
/
Revised: 21 August 2024
/
Accepted: 22 August 2024
/
Published: 27 August 2024
(This article belongs to the Section Veterinary Clinical Studies)
Simple Summary
In this study, a Common Objects in Context dataset of ovine pulmonary adenocarcinoma pathological images was constructed based on 7167 annotated typical lesions from 61,063 lung pathological images of ovine pulmonary adenocarcinoma. This study aimed to develop a mask regional convolutional neural network model for the localization and pathological diagnosis of ovine pulmonary adenocarcinoma lesions. The model achieved a mean average specificity of 0.573 and an average sensitivity of 0.745, with consistency rates of 100% for junior pathologists and 96.5% for senior pathologists in the diagnosis of ovine pulmonary adenocarcinoma. The successful development of this model not only facilitates the rapid diagnosis of ovine pulmonary adenocarcinoma by different personnel in practical applications but also lays a foundation for the transition from traditional pathology to digital pathology in the livestock industry.
Abstract
Ovine pulmonary adenocarcinoma (OPA) is a contagious lung tumour caused by the Jaagsiekte Sheep Retrovirus (JSRV). Histopathological diagnosis is the gold standard for OPA diagnosis. However, interpretation of traditional pathology images is complex and operator dependent. The mask regional convolutional neural network (Mask R-CNN) has emerged as a valuable tool in pathological diagnosis. This study utilized 54 typical OPA whole slide images (WSI) to extract 7167 typical lesion images containing OPA to construct a Common Objects in Context (COCO) dataset for OPA pathological images. The dataset was categorized into training and test sets (8:2 ratio) for model training and validation. Mean average specificity (mASp) and average sensitivity (ASe) were used to evaluate model performance. Six WSI-level pathological images (three OPA and three non-OPA images), not included in the dataset, were used for anti-peeking model validation. A random selection of 500 images, not included in the dataset establishment, was used to compare the performance of the model with assessment by pathologists. Accuracy, sensitivity, specificity, and concordance rate were evaluated. The model achieved a mASp of 0.573 and an ASe of 0.745, demonstrating effective lesion detection and alignment with expert annotation. In Anti-Peeking verification, the model showed good performance in locating OPA lesions and distinguished OPA from non-OPA pathological images. In the random 500-image diagnosis, the model achieved 92.8% accuracy, 100% sensitivity, and 88% specificity. The agreement rates between junior and senior pathologists were 100% and 96.5%, respectively. In conclusion, the Mask R-CNN-based OPA diagnostic model developed for OPA facilitates rapid and accurate diagnosis in practical applications.
