Episodic mass loss in binary evolution to the Wolf-Rayet phase: Keck and HST proper motions of RY Scuti's nebula

Binary mass transfer via Roche-lobe overflow (RLOF) is a key channel for producing stripped-envelope Wolf-Rayet (WR) stars and may be critical to account for SN Ib/c progenitors. RY Scuti is an extremely rare example of a massive binary star caught in this brief but important phase. Its toroidal nebula indicates equatorial mass loss during RLOF, while the mass-gaining star is apparently embedded in an opaque accretion disk. RY Scuti's toroidal nebula has two components: an inner ionised double-ring system, and an outer dust torus that is twice the size of the ionised rings. We present two epochs of Lband Keck NGS-AO images of the dust torus, plus three epochs of HST images of the ionised gas rings. Proper motions show that the inner ionised rings and the outer dust torus came from two separate ejection events roughly 130 and 250 yr ago. This suggests that RLOF in massive contact binaries can be accompanied by eruptive and episodic burst of mass loss, reminiscent of LBVs. We speculate that the repeating outbursts may arise in the mass gainer from instabilities associated with a high accretion rate. If discrete mass-loss episodes in other RLOF binaries are accompanied by luminous outbursts, they might contribute to the population of extragalactic optical transients. When RLOF ends for RY Scuti, the overluminous mass gainer, currently surrounded by an accretion disk, will probably become a B[e] supergiant and may outshine the hotter mass-donor star that should die as a Type Ib/c supernova.Comment: 15 pages, 7 figures, submitted to MNRA

Automatic segmentation of MR brain images of preterm infants using supervised classification

\u3cp\u3ePreterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data from the partially annotated images resulted in slightly lower Dice coefficients, the performance in all experiments was close to that of a second human expert (0.93 for WM, 0.79 for GM and 0.86 for CSF for the images acquired at 30weeks, and 0.94 for WM, 0.76 for GM and 0.87 for CSF for the images acquired at 40weeks). These results show that the presented method is robust to age and acquisition protocol and that it performs accurate segmentation of WM, GM, and CSF when the training data is extracted from complete annotations as well as when the training data is extracted from partial annotations only. This extends the applicability of the method by reducing the time and effort necessary to create training data in a population with different characteristics.\u3c/p\u3