Design and evaluation of a yield monitoring system for combinable crops

The existence of spatial variability within fields can be beneficial if inputs for arable crop are given to the field according to locally determined requirements. While yield mapping has become an important part of precision farming strategies, the goal of this paper is to plot a yield map by the application of yield monitoring components. A yield monitoring system capable of providing sufficient reliable data to plot a yield map for small grain fields in central regions of Iran was developed. The system consisted of an impact flow sensor determining the mass flow of grain, the GPS receiver determining geographical position of the machine, two shaft encoders measuring the speed of the combine, an ultrasonic sensor measuring the actual cutting width, and a data logger. The mass flow sensor consisted of a load cell and an impact plate which was exposed to the predominant grain flow from the clean grain elevator. This sensor was positioned in the transition housing between the elevator and the loading auger of the clean grain tank. The calibration of the sensor related the force on the sensor to the mass flow rate of grain. The yield data were used with information generated by the GPS receiver and a yield map was created. At last, the correlation between the maps and the data collected using traditional method was found which supports the reliability of the monitoring system

Mitosis domain generalization in histopathology images -- The MIDOG challenge

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As a test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were given. The best approaches performed on an expert level, with the winning algorithm yielding an F_1 score of 0.748 (CI95: 0.704-0.781). In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance.Comment: 19 pages, 9 figures, summary paper of the 2021 MICCAI MIDOG challeng

MiNuGAN: Dual Segmentation of Mitoses and Nuclei Using Conditional GANs on Multi-center Breast H&E Images

Breast cancer is the second most commonly diagnosed type of cancer among women as of 2021. Grading of histopathological images is used to guide breast cancer treatment decisions and a critical component of this is a mitotic score, which is related to tumor aggressiveness. Manual mitosis counting is an extremely tedious manual task, but automated approaches can be used to overcome inefficiency and subjectivity. In this paper, we propose an automatic mitosis and nuclear segmentation method for a diverse set of H&E breast cancer pathology images. The method is based on a conditional generative adversarial network to segment both mitoses and nuclei at the same time. Architecture optimizations are investigated, including hyper parameters and the addition of a focal loss. The accuracy of the proposed method is investigated using images from multiple centers and scanners, including TUPAC16, ICPR14 and ICPR12 datasets. In TUPAC16, we use 618 carefully annotated images of size 256×256 scanned at 40×. TUPAC16 is used to train the model, and segmentation performance is measured on the test set for both nuclei and mitoses. Results on 200 held-out testing images from the TUPAC16 dataset were mean DSC = 0.784 and 0.721 for nuclear and mitosis, respectively. On 202 ICPR12 images, mitosis segmentation accuracy had a mean DSC = 0.782, indicating the model generalizes well to unseen datasets. For datasets that had mitosis centroid annotations, which included 200 TUPAC16, 202 ICPR12 and 524 ICPR14, a mean F1-score of 0.854 was found indicating high mitosis detection accuracy