Image segmentation - handcrafted features vs DNN?
You have an object vs background task, i.e. two classes and instance (or m.b. class segmentation problem). If you have a labelled dataset, you can finetune one of the publicly available networks (e.g. FCN, MaskRCNN) to it to produce an image mask (i.e. an array size of the input image where every pixel is labelled as either object or background). So yeah, in 2018 feature engineering can't beat a (fully convolutional, 140M parameter, 16-layer) ConvNN.