Robust Medical Instrument Segmentation Challenge 2019

Abstract

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).

arXiv commentA pre-print

References in corpus (10)

• Adam: A Method for Stochastic Optimization
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
• Why rankings of biomedical image analysis competitions should be interpreted with care
• nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
• Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
• 2017 Robotic Instrument Segmentation Challenge
• Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
• OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images
• BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation

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