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  • Conference paper
    Uslu F, Varela M, Bharath AA, 2020,

    A semi-automatic method to segment the left atrium in MR volumes with varying slice numbers.

    , 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Publisher: IEEE, Pages: 1198-1202

    Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with dramatic increases in mortality and morbidity. Atrial cine MR images are increasingly used in the management of this condition, but there are few specific tools to aid in the segmentation of such data. Some characteristics of atrial cine MR (thick slices, variable number of slices in a volume) preclude the direct use of traditional segmentation tools. When combined with scarcity of labelled data and similarity of the intensity and texture of the left atrium (LA) to other cardiac structures, the segmentation of the LA in CINE MRI becomes a difficult task. To deal with these challenges, we propose a semi-automatic method to segment the left atrium (LA) in MR images, which requires an initial user click per volume. The manually given location information is used to generate a chamber location map to roughly locate the LA, which is then used as an input to a deep network with slightly over 0.5 million parameters. A tracking method is introduced to pass the location information across a volume and to remove unwanted structures in segmentation maps. According to the results of our experiments conducted in an in-house MRI dataset, the proposed method outperforms the U-Net [1] with a margin of 20 mm on Hausdorff distance and 0.17 on Dice score, with limited manual interaction.
  • Conference paper
    Varela Anjari M, Queiros S, Anjari M, Correia T, King A, Bharath A, Lee Jet al., 2020,

    Strain maps of the left atrium imagedwith a novel high-resolutionCINEMRI protocol*

    , 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology, Publisher: IEEE, Pages: 1178-1181, ISSN: 1557-170X

    To date, regional atrial strains have not been imaged in vivo, despite their potential to provide useful clinical information. To address this gap, we present a novel CINE MRI protocol capable of imaging the entire left atrium at an isotropic 2-mm resolution in one single breath-hold.As proof of principle, we acquired data in 10 healthy volunteers and 2 cardiovascular patients using this technique.We also demonstrated how regional atrial strains can be estimated from this data following a manual segmentation of the left atrium using automatic image tracking techniques.The estimated principal strains vary smoothly across the left atrium and have a similar magnitude to estimates reported in the literature.
  • Journal article
    Dai T, Arulkumaran K, Gerbert T, Tukra S, Behbahani F, Bharath AAet al., 2020,

    Analysing deep reinforcement learning agents trained with domain randomisation

    Deep reinforcement learning has the potential to train robots to performcomplex tasks in the real world without requiring accurate models of the robotor its environment. A practical approach is to train agents in simulation, andthen transfer them to the real world. One popular method for achievingtransferability is to use domain randomisation, which involves randomlyperturbing various aspects of a simulated environment in order to make trainedagents robust to the reality gap. However, less work has gone intounderstanding such agents - which are deployed in the real world - beyond taskperformance. In this work we examine such agents, through qualitative andquantitative comparisons between agents trained with and without visual domainrandomisation. We train agents for Fetch and Jaco robots on a visuomotorcontrol task and evaluate how well they generalise using different testingconditions. Finally, we investigate the internals of the trained agents byusing a suite of interpretability techniques. Our results show that the primaryoutcome of domain randomisation is more robust, entangled representations,accompanied with larger weights with greater spatial structure; moreover, thetypes of changes are heavily influenced by the task setup and presence ofadditional proprioceptive inputs. Additionally, we demonstrate that our domainrandomised agents require higher sample complexity, can overfit and moreheavily rely on recurrent processing. Furthermore, even with an improvedsaliency method introduced in this work, we show that qualitative studies maynot always correspond with quantitative measures, necessitating the combinationof inspection tools in order to provide sufficient insights into the behaviourof trained agents.
  • Journal article
    Uslu F, Bass C, Bharath AA, 2020,

    PERI-Net: a parameter efficient residual inception network for medical image segmentation

    , TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, Vol: 28, Pages: 2261-2277, ISSN: 1300-0632
  • Journal article
    Kothari S, Gionfrida L, Bharath AA, Abraham Set al., 2019,

    Artificial Intelligence (AI) and rheumatology: a potential partnership

    , RHEUMATOLOGY, Vol: 58, Pages: 1894-1895, ISSN: 1462-0324

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