A deep level set method for image segmentation

This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can incorporatesmoothing and prior information to achieve an accurate segmentation.Furthermore, different than using the level set model as a post-processingtool, we integrate it into the training phase to fine-tune the FCN. Thisallows the use of unlabeled data during training in a semi-supervisedsetting. Using two types of medical imaging data (liver CT and left ven-tricle MRI data), we show that the integrated method achieves goodperformance even when little training data is available, outperformingthe FCN or the level set model alone

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks

HeMIS: Hetero-Modal Image Segmentation

We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.Comment: Accepted as an oral presentation at MICCAI 201

Star Formation in Sculptor Group Dwarf Irregular Galaxies and the Nature of "Transition" Galaxies

We present new H-alpha narrow band imaging of the HII regions in eight Sculptor Group dwarf irregular (dI) galaxies. Comparing the Sculptor Group dIs to the Local Group dIs, we find that the Sculptor Group dIs have, on average, lower values of SFR when normalized to either galaxy luminosity or gas mass (although there is considerable overlap between the two samples). The properties of ``transition'' (dSph/dIrr) galaxies in Sculptor and the Local Group are also compared and found to be similar. The transition galaxies are typically among the lowest luminosities of the gas rich dwarf galaxies. Relative to the dwarf irregular galaxies, the transition galaxies are found preferentially nearer to spiral galaxies, and are found nearer to the center of the mass distribution in the local cloud. While most of these systems are consistent with normal dI galaxies which currently exhibit temporarily interrupted star formation, the observed density-morphology relationship (which is weaker than that observed for the dwarf spheroidal galaxies) indicates that environmental processes such as ``tidal stirring'' may play a role in causing their lower SFRs.Comment: 35 pages, 10 figures, accepted for Feb 2003 AJ, companion to astro-ph/021117

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, more similar to a clinical setting than what is typically provided in multiple sclerosis segmentation challenges. Furthermore, we evaluate a prototype naive combination of the two methods, which refines the final segmentation. All methods were trained on 32 patients, and the evaluation was performed on a pure test set of 73 cases. Results show low lesion-wise false positives (30%) for the deep learning architecture, whereas the shallow architecture yields the best Dice coefficient (63%) and volume difference (19%). Combining both shallow and deep architectures further improves the lesion-wise metrics (69% and 26% lesion-wise true and false positive rate, respectively).Comment: Accepted to the MICCAI 2018 Brain Lesion (BrainLes) worksho

Comment Regarding the Functional Form of the Schmidt Law

Star formation rates on the galactic scale are described phenomenologically by two distinct relationships, as emphasized recently by Elmegreen (2002). The first of these is the Schmidt law, which is a power-law relation between the star formation rate and the column density. The other relationship is that there is a cutoff in the gas density below which star formation shuts off. The purpose of this paper is to argue that 1) these two relationships can be accommodated by a single functional form of the Schmidt law, and 2) this functional form is motivated by the hypothesis that star formation is a critical phenomenon, and that as a corollary, 3) the existence of a sharp cutoff may thus be an emergent property of galaxies, as was argued by Seiden (1983), as opposed to the classical view that this cutoff is due to an instability criterion.Comment: 14 pages, 3 figures, in press, New Astronomy. Figs provided in original (png) format as well as ps format for ps/pdf generatio

The Centurion 18 telescope of the Wise Observatory

We describe the second telescope of the Wise Observatory, a 0.46-m Centurion 18 (C18) installed in 2005, which enhances significantly the observing possibilities. The telescope operates from a small dome and is equipped with a large-format CCD camera. In the last two years this telescope was intensively used in a variety of monitoring projects. The operation of the C18 is now automatic, requiring only start-up at the beginning of a night and close-down at dawn. The observations are mostly performed remotely from the Tel Aviv campus or even from the observer's home. The entire facility was erected for a component cost of about 70k$ and a labor investment of a total of one man-year. We describe three types of projects undertaken with this new facility: the measurement of asteroid light variability with the purpose of determining physical parameters and binarity, the following-up of transiting extrasolar planets, and the study of AGN variability. The successful implementation of the C18 demonstrates the viability of small telescopes in an age of huge light-collectors, provided the operation of such facilities is very efficient.Comment: 16 pages, 13 figures, some figures quality was degraded, accepted for publication in Astrophysics and Space Scienc

Galaxy Candidates in the Zone of Avoidance

Motivated by recent discoveries of nearby galaxies in the Zone of Avoidance, we conducted a pilot study of galaxy candidates at low Galactic latitude, near Galactic longitude $l \sim 135^0$, where the Supergalactic Plane is crossed by the Galactic Plane. We observed with the 1m Wise Observatory in the I-band 18 of the `promising' candidates identified by visual examination of Palomar red plates by Hau et al. (1995). A few candidates were also observed in R or B bands, or had spectroscopic observations performed at the Isaac Newton Telescope and at the Wise Observatory. Our study suggests that there are probably 10 galaxies in this sample. We also identify a probable Planetary Nebula. The final confirmation of the nature of these sources must await the availability of full spectroscopic information. The success rate of $\sim 50$ in identifying galaxies at Galactic latitude $|b|<5^\circ$ indicates that the ZOA is a bountiful region to discover new galaxies.Comment: 11 pages; Latex + 5 figures (gif format), Submitted to MNRA