The Atlantic salmon maturation process has been studied for decades to increase the quantity and quality of the production in farming facilities. An important topic in this context is the salmon egg maturation process. Ultrasound imaging is considered an effective tool for monitoring the egg development stage of salmon, but manual inspection is time-consuming and dependent on operator experience. We propose a method for automated monitoring of the egg maturation stage in salmon using deep learning, providing complimentary decisions on egg morphology. A segmentation network was developed to solve the challenge of separating and measuring individual eggs in the ovary. The segmentation part was combined with a classification network to determine the maturation stage of the eggs. Our model was able to segment eggs and classify their development stage with over 88% accuracy, outperforming established methods designed for similar tasks. A real-time application was developed which provided an estimation of size and maturity stage while scanning. The egg state estimation showed potential for replacing manual evaluations and can enable fully automatic evaluation of maturation in Atlantic salmon.
The Atlantic salmon maturation process has been studied for decades to increase the quantity and quality of the production in farming facilities. An important topic in this context is salmon egg maturation process. Ultrasound imaging is considered an effective tool for monitoring the egg development stage of salmon, but manual inspection is time-consuming and highly dependent on operator experience. We propose a method for automated monitoring of the egg maturation stage in salmon using deep learning, providing complimentary decisions on egg morphology. A segmentation network was developed to solve the challenge of separating and measuring individual eggs in the ovary. The segmentation part was combined with a classification network to determine the maturation stage of the eggs. Our model was able to segment eggs and classify their development stage with over 88% accuracy, outperforming established methods designed for similar tasks. A real-time application was developed which provided an estimation of size and maturity stage while scanning. The egg state estimation showed potential for replacing manual evaluations and can enable fully automatic evaluation of maturation in Atlantic salmon.
The Atlantic salmon maturation process has been studied for decades to increase the quantity and quality of the production in farming facilities. An important topic in this context is the salmon egg maturation process. Ultrasound imaging is considered an effective tool for monitoring the egg development stage of salmon, but manual inspection is time-consuming and dependent on operator experience. We propose a method for automated monitoring of the egg maturation stage in salmon using deep learning, providing complimentary decisions on egg morphology. A segmentation network was developed to solve the challenge of separating and measuring individual eggs in the ovary. The segmentation part was combined with a classification network to determine the maturation stage of the eggs. Our model was able to segment eggs and classify their development stage with over 88% accuracy, outperforming established methods designed for similar tasks. A real-time application was developed which provided an estimation of size and maturity stage while scanning. The egg state estimation showed potential for replacing manual evaluations and can enable fully automatic evaluation of maturation in Atlantic salmon.
Salmon breeding companies control the egg stripping period through environmental change, which triggers the need to identify the state of maturation. Ultrasound imaging of the salmon ovary is a proven non-invasive tool for this purpose; however, the process is laborious, and the interpretation of the ultrasound scans is subjective. Real-time ultrasound image segmentation of Atlantic salmon ovary provides an opportunity to overcome these limitations. However, several application challenges need to be addressed to achieve this goal. These challenges include the potential for false-positive and false-negative predictions, accurate prediction of attenuated lower ovary parts and resolution of inconsistencies in predicted ovary shape.
