Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

Calcium imaging is a technique for observing neuron activity as a series of images showing indicator fluorescence over time. Manually segmenting neurons is time-consuming, leading to research on automated calcium imaging segmentation (ACIS). We evaluated several deep learning models for ACIS on the Neurofinder competition datasets and report our best model: U-Net2DS, a fully convolutional network that operates on 2D mean summary images. U-Net2DS requires minimal domain-specific pre/post-processing and parameter adjustment, and predictions are made on full $512\times512$ images at $\approx$9K images per minute. It ranks third in the Neurofinder competition ($F_1=0.569$) and is the best model to exclusively use deep learning. We also demonstrate useful segmentations on data from outside the competition. The model's simplicity, speed, and quality results make it a practical choice for ACIS and a strong baseline for more complex models in the future.Comment: Accepted to 3rd Workshop on Deep Learning in Medical Image Analysis (http://cs.adelaide.edu.au/~dlmia3/

The impact of pulsed electric field on the extraction of bioactive compounds from beetroot

Beetroot is a root vegetable rich in different bioactive components, such as vitamins, minerals, phenolics, carotenoids, nitrate, ascorbic acids, and betalains, that can have a positive effect on human health. The aim of this work was to study the influence of the pulsed electric field (PEF) at different electric field strengths (4.38 and 6.25 kV/cm), pulse number 10\u201330, and energy input 0\u201312.5 kJ/kg as a pretreatment method on the extraction of betalains from beetroot. The obtained results showed that the application of PEF pre-treatment significantly (p < 0.05) influenced the efficiency of extraction of bioactive compounds from beetroot. The highest increase in the content of betalain compounds in the red beet\u2019s extract (betanin by 329%, vulgaxanthin by 244%, compared to the control sample), was noted for 20 pulses of electric field at 4.38 kV/cm of strength. Treatment of the plant material with a PEF also resulted in an increase in the electrical conductivity compared to the non-treated sample due to the increase in cell membrane permeability, which was associated with leakage of substances able to conduct electricity, including mineral salts, into the intercellular space

FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net

In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model, known as U-net. U-net is firstly modified by adding widely used batch normalization and residual block (named as BRU-net) to improve the efficiency of model training. Based on BRU-net, we further introduce a dynamically weighted cross-entropy loss function. The weighting scheme is calculated based on the pixel-wise prediction accuracy during the training process. Assigning higher weights to pixels with lower segmentation accuracies enables the network to learn more from poorly predicted image regions. Our method is named as feedback weighted U-net (FU-net). We have evaluated our method based on T1- weighted brain MRI for the segmentation of midbrain and substantia nigra, where the number of pixels in each class is extremely unbalanced to each other. Based on the dice coefficient measurement, our proposed FU-net has outperformed BRU-net and U-net with statistical significance, especially when only a small number of training examples are available. The code is publicly available in GitHub (GitHub link: https://github.com/MinaJf/FU-net).Comment: Accepted for publication at International Conference on Image and Graphics (ICIG 2019

A Benchmark for endoluminal scene segmentation of colonoscopy images

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization

High resolution crystal structure of Z DNA in complex with Cr3 cations

This work is part of our project aimed at characterizing metal-binding properties of left-handed Z-DNA helices. The three Cr(3+) cations found in the asymmetric unit of the d(CGCGCG)(2)–Cr(3+) crystal structure do not form direct coordination bonds with atoms of the Z-DNA molecule. Instead, the hydrated Cr(3+) ions are engaged in outer-sphere interactions with phosphate groups and O6 and N7 guanine atoms of the DNA. The Cr(3+)(1) and Cr(3+)(2) ions have disordered coordination spheres occupied by six water molecules each. These partial-occupancy chromium cations are 2.354(15) Å apart and are bridged by three water molecules from their hydration spheres. The Cr(3+)(3) cation has distorted square pyramidal geometry. In addition to the high degree of disorder of the DNA backbone, alternate conformations are also observed for the deoxyribose and base moieties of the G2 nucleotide. Our work illuminates the question of conformational flexibility of Z-DNA and its interaction mode with transition-metal cations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00775-015-1247-5) contains supplementary material, which is available to authorized users

Design of healthy snack based on kiwifruit

Kiwifruit is an excellent source of vitamin C and other bioactive compounds, which contribute to its high antioxidant activity. However, the fruits with small size and low weight are considered waste and are unprofitable; therefore, the production of healthy kiwifruit-based dried snacks, which contain a lot of health-beneficial ingredients, could be a viable alternative for their use. The aim of this study was to develop formulations and methods to produce attractive and nutritionally valuable dried snacks based on yellow kiwifruit. Three different puree formulations (kiwifruit; fennel; and strawberry, lemon, or spinach) with or without addition of sugar were subjected to two drying methods: freeze-drying (fruit bars) and conventional hot air drying (fruit leathers). The obtained products were analysed for their content of total polyphenols (TPs), flavonoids, and vitamin C, as well as their antioxidant activity. The results showed that snacks prepared by freeze-drying (fruit bars) presented higher TP, vitamin C, and flavonoids content than those prepared by convective drying; however, the antioxidant activity did not always follow this trend. The amount of bioactive compounds depended on the formulation used for the preparation of snacks. The effect of the sugar addition seems to be strictly related to the mix used and specific bioactive compound investigated

Design of healthy snack based on kiwifruit

Kiwifruit is an excellent source of vitamin C and other bioactive compounds, which contribute to its high antioxidant activity. However, the fruits with small size and low weight are considered waste and are unprofitable; therefore, the production of healthy kiwifruit-based dried snacks, which contain a lot of health-beneficial ingredients, could be a viable alternative for their use. The aim of this study was to develop formulations and methods to produce attractive and nutritionally valuable dried snacks based on yellow kiwifruit. Three different puree formulations (kiwifruit; fennel; and strawberry, lemon, or spinach) with or without addition of sugar were subjected to two drying methods: freeze-drying (fruit bars) and conventional hot air drying (fruit leathers). The obtained products were analysed for their content of total polyphenols (TPs), flavonoids, and vitamin C, as well as their antioxidant activity. The results showed that snacks prepared by freeze-drying (fruit bars) presented higher TP, vitamin C, and flavonoids content than those prepared by convective drying; however, the antioxidant activity did not always follow this trend. The amount of bioactive compounds depended on the formulation used for the preparation of snacks. The effect of the sugar addition seems to be strictly related to the mix used and specific bioactive compound investigated

Needles in haystacks: On classifying tiny objects in large images

In some computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images. However, most Convolutional Neural Networks (CNNs) for image classification were developed and analyzed using biased datasets that contain large objects, most often, in central image positions. To assess whether classical CNN architectures work well for tiny object classification we build a comprehensive testbed containing two datasets: one derived from MNIST digits and other from histopathology images. This testbed allows us to perform controlled experiments to stress-test CNN architectures using a broad spectrum of signal-to-noise ratios. Our observations suggest that: (1) There exists a limit to signal-to-noise below which CNNs fail to generalize and that this limit is affected by dataset size - more data leading to better performances; however, the amount of training data required for the model to generalize scales rapidly with the inverse of the object-to-image ratio (2) in general, higher capacity models exhibit better generalization; (3) when knowing the approximate object sizes, adapting receptive field is beneficial; and (4) for very small signal-to-noise ratio the choice of global pooling operation affects optimization, whereas for relatively large signal-to-noise values, all tested global pooling operations exhibit similar performance