Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping.

The rise of self-supervised learning (SSL) methods in recent years presents an opportunity to leverage unlabeled and domain-specific datasets generated by image-based plant phenotyping platforms to accelerate plant breeding programs. Despite the surge of research on SSL, there has been a scarcity of research exploring the applications of SSL to image-based plant phenotyping tasks, particularly detection and counting tasks. We address this gap by benchmarking the performance of 2 SSL methods-momentum contrast (MoCo) v2 and dense contrastive learning (DenseCL)-against the conventional supervised learning method when transferring learned representations to 4 downstream (target) image-based plant phenotyping tasks: wheat head detection, plant instance detection, wheat spikelet counting, and leaf counting.

Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns.

Deep learning has shown potential in domains with large-scale annotated datasets. However, manual annotation is expensive, time-consuming, and tedious. Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances, such as in plant images.

Iterative image segmentation of plant roots for high-throughput phenotyping.

Accurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems. Various approaches to image segmentation exist but many of them are not well suited to the thin and reticulated structures characteristic of root systems. The findings presented here describe an approach to RSA segmentation that takes advantage of the inherent structural properties of the root system, a segmentation network architecture we call ITErRoot.

Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method.

To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps. Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position.

Genes responsive to rapamycin and serum deprivation are clustered on chromosomes and undergo reorganization within local chromatin environments.

We previously demonstrated that genome reorganization, through chromosome territory repositioning, occurs concurrently with significant changes in gene expression in normal primary human fibroblasts treated with the drug rapamycin, or stimulated into quiescence. Although these events occurred concomitantly, it is unclear how specific changes in gene expression relate to reorganization of the genome at higher resolution. We used computational analyses, genome organization assays, and microscopy, to investigate the relationship between chromosome territory positioning and gene expression.

Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network.

With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images.

Image-based search and retrieval for biface artefacts using features capturing archaeologically significant characteristics.

Archaeologists are currently producing huge numbers of digitized photographs to record and preserve artefact finds. These images are used to identify and categorize artefacts and reason about connections between artefacts and perform outreach to the public. However, finding specific types of images within collections remains a major challenge.

LBP-Based Segmentation of Defocus Blur.

Defocus blur is extremely common in images captured using optical imaging systems. It may be undesirable, but may also be an intentional artistic effect, thus it can either enhance or inhibit our visual perception of the image scene. For tasks, such as image restoration and object recognition, one might want to segment a partially blurred image into blurred and non-blurred regions.

Rapamycin reduces fibroblast proliferation without causing quiescence and induces STAT5A/B-mediated cytokine production.

Rapamycin is a well-known inhibitor of the Target of Rapamycin (TOR) signaling cascade; however, the impact of this drug on global genome function and organization in normal primary cells is poorly understood. To explore this impact, we treated primary human foreskin fibroblasts with rapamycin and observed a decrease in cell proliferation without causing cell death. Upon rapamycin treatment chromosomes 18 and 10 were repositioned to a location similar to that of fibroblasts induced into quiescence by serum reduction.

Enhancement of textural differences based on morphological component analysis.

This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture.

Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns.

In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.

Level set segmentation of bovine corpora lutea in ex situ ovarian ultrasound images.

Background: The objective of this study was to investigate the viability of level set image segmentation methods for the detection of corpora lutea (corpus luteum, CL) boundaries in ultrasonographic ovarian images. It was hypothesized that bovine CL boundaries could be located within 1-2 mm by a level set image segmentation methodology.

Methods: Level set methods embed a 2D contour in a 3D surface and evolve that surface over time according to an image-dependent speed function.

Enhancing ultrasound texture differences for developing an in vivo 'virtual histology' approach to bovine ovarian imaging.

A 'virtual histology' can be thought of as the 'staining' of a digital ultrasound image via image processing techniques in order to enhance the visualisation of differences in the echotexture of different types of tissues. Several candidate image-processing algorithms for virtual histology using ultrasound images of the bovine ovary were studied. The candidate algorithms were evaluated qualitatively for the ability to enhance the visual differences in intra-ovarian structures and quantitatively, using standard texture description features, for the ability to increase statistical differences in the echotexture of different ovarian tissues.

Evaluation of texture features for analysis of ovarian follicular development.

We examined the echotexture in ultrasonographic images of the wall of dominant ovulatory follicles in women during natural menstrual cycles and dominant anovulatory follicles which developed in women using oral contraceptives (OC). Ovarian follicles in women are fluid-filled structures in the ovary that contain oocytes (eggs). Dominant follicles are physiologically selected for preferential development and ovulation.