Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

By Paul F. Jaeger, Simon A. A. Kohl, Sebastian Bickelhaupt, Fabian Isensee, Tristan Anselm Kuder, Heinz-Peter Schlemmer, Klaus Hermann Maier-Hein. 2018

233 citations recorded by the source corpus.

The task of localizing and categorizing objects in medical images often remains formulated as a semantic segmentation problem. This approach, however, only indirectly solves the coarse localization task by predicting pixel-level scores, requiring ad-hoc heuristics when mapping back to object-level scores. State-of-the-art object detectors on the other hand, allow for individual object scoring in an end-to-end fashion, while ironically trading in the ability to exploit the full pixel-wise supervision signal. This can be particularly disadvantageous in the setting of medical image analysis, where data sets are notoriously small. In this paper, we propose Retina U-Net, a simple architecture, which naturally fuses the Retina Net one-stage detector with the U-Net architecture widely used for semantic segmentation in medical images. The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors. We evaluate the importance of full segmentation supervision on two medical data sets, provide an in-depth analysis on a series of toy experiments and show how the corresponding performance gain grows in the limit of small data sets. Retina U-Net yields strong detection performance only reached by its more complex two-staged counterparts. Our framework including all methods implemented for operation on 2D and 3D images is available at github.com/pfjaeger/medicaldetectiontoolkit.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Medical Imaging

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Citations in the corpus

Pix2seq: A Language Modeling Framework for Object Detection (2021, background): “, 2020), to medical image analysis (Jaeger et al., 2020), to agriculture (Sa et al.”
Imbalance Problems in Object Detection: A Review (2019, background): “surveillance [1], [2], autonomous driving [3], [4], medical decision making [5], [6], and many problems in robotics [7], [8], [9], [10], [11], [12].”
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation (2019, background): “Similarly, Jaeger et al. (2018); Huang et al. (2018b) propose a joint segmentation and detection framework.”
A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit (2021, background): “Detecting anomalies such as glaucoma, breast, and lung lesions, for instance, have been explored from the medical object-detection perspective in [63,64].”