Browsing by Author "Jaremko, Jacob L."

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Developmental hip dysplasia diagnosis at three-dimensional US: a multicenter study
    (2018) Zonoobi, Dornoosh; Hareendranathan, Abhilash; Mostofi, Emanuel; Mabee, Myles; Pasha, Saba; Cobzas, Dana; Rao, Padma; Dulai, Sukhdeep K.; Kapur, Jeevesh; Jaremko, Jacob L.
    Purpose: To validate accuracy of diagnosis of developmental dysplasia of the hip (DDH) from geometric properties of acetabular shape extracted from three-dimensional (3D) ultrasonography (US).
  • Thumbnail Image
    Item
    End-to-end detection-segmentation network with ROI convolution
    (2018) Zhang, Zichen; Tang, Min; Cobzas, Dana; Zonoobi, Dornoosh; Jagersand, Martin; Jaremko, Jacob L.
    We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation.
  • Thumbnail Image
    Item
    Segmentation-by-detection: a cascade network for volumetric medical image segmentation
    (2018) Tang, Min; Zhang, Zichen; Cobzas, Dana; Jagersand, Martin; Jaremko, Jacob L.
    We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise. This is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net.