EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation

TL;DR

This paper introduces EnMcGAN, an adversarial ensemble learning method that significantly improves 3D renal structure segmentation accuracy, aiding preoperative planning and intraoperative guidance in renal cancer treatment.

Contribution

It presents a novel multi-windowing committee, multi-condition GAN, and adversarial weighted ensemble module for enhanced 3D renal structure segmentation.

Findings

Achieved a mean Dice coefficient of 84.6% on 122 patients.

Demonstrated improved segmentation accuracy over existing methods.

Showed clinical relevance in renal cancer treatment applications.

Abstract

3D complete renal structures(CRS) segmentation targets on segmenting the kidneys, tumors, renal arteries and veins in one inference. Once successful, it will provide preoperative plans and intraoperative guidance for laparoscopic partial nephrectomy(LPN), playing a key role in the renal cancer treatment. However, no success has been reported in 3D CRS segmentation due to the complex shapes of renal structures, low contrast and large anatomical variation. In this study, we utilize the adversarial ensemble learning and propose Ensemble Multi-condition GAN(EnMcGAN) for 3D CRS segmentation for the first time. Its contribution is three-fold. 1)Inspired by windowing, we propose the multi-windowing committee which divides CTA image into multiple narrow windows with different window centers and widths enhancing the contrast for salient boundaries and soft tissues. And then, it builds an ensemble segmentation model on these narrow windows to fuse the segmentation superiorities and improve whole segmentation quality. 2)We propose the multi-condition GAN which equips the segmentation model with multiple discriminators to encourage the segmented structures meeting their real shape conditions, thus improving the shape feature extraction ability. 3)We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results. 122 patients are enrolled in this study and the mean Dice coefficient of the renal structures achieves 84.6%. Extensive experiments with promising results on renal structures reveal powerful segmentation accuracy and great clinical significance in renal cancer treatment.