Automatic Lacunae Localization in Placental Ultrasound Images via Layer Aggregation

Accurate localization of structural abnormalities is a precursor for image-based prenatal assessment of adverse conditions. For clinical screening and diagnosis of abnormally invasive placenta (AIP), a life-threatening obstetric condition, qualitative and quantitative analysis of ultrasonic patterns correlated to placental lesions such as placental lacunae (PL) is challenging and time-consuming to perform even for experienced sonographers. There is a need for automated placental lesion localization that does not rely on expensive human annotations such as detailed manual segmentation of anatomical structures. In this paper, we investigate PL localization in 2D placental ultrasound images. First, we demonstrate the effectiveness of generating confidence maps from weak dot annotations in localizing PL as an alternative to expensive manual segmentation. Then we propose a layer aggregation structure based on iterative deep aggregation (IDA) for PL localization. Models with this structure were evaluated with 10-fold cross-validations on an AIP database (containing 3,440 images with 9,618 labelled PL from 23 AIP and 11 non-AIP participants). Experimental results demonstrate that the model with the proposed structure yielded the highest mean average precision (mAP = 35.7%), surpassing all other baseline models (32.6%, 32.2%, 29.7%). We argue that features from shallower stages can contribute to PL localization more effectively using the proposed structure. To our knowledge, this is the first successful application of machine learning to placental lesion analysis and has the potential to be adapted for other clinical scenarios in breast, liver, and prostate cancer imaging.