The kidney biopsy is the gold standard for the diagnosis of kidney diseases.
Lesion scores made by expert renal pathologists are semi-quantitative and
suffer from high inter-observer variability. Automatically obtaining statistics
per segmented anatomical object, therefore, can bring significant benefits in
reducing labor and this inter-observer variability. Instance segmentation for a
biopsy, however, has been a challenging problem due to (a) the on average large
number (around 300 to 1000) of densely touching anatomical structures, (b) with
multiple classes (at least 3) and (c) in different sizes and shapes. The
currently used instance segmentation models cannot simultaneously deal with
these challenges in an efficient yet generic manner. In this paper, we propose
the first anchor-free instance segmentation model that combines diffusion
models, transformer modules, and RCNNs (regional convolution neural networks).
Our model is trained on just one NVIDIA GeForce RTX 3090 GPU, but can
efficiently recognize more than 500 objects with 3 common anatomical object
classes in renal biopsies, i.e., glomeruli, tubuli, and arteries. Our data set
consisted of 303 patches extracted from 148 Jones' silver-stained renal whole
slide images (WSIs), where 249 patches were used for training and 54 patches
for evaluation. In addition, without adjustment or retraining, the model can
directly transfer its domain to generate decent instance segmentation results
from PAS-stained WSIs. Importantly, it outperforms other baseline models and
reaches an AP 51.7% in detection as the new state-of-the-art.

该研究提出了一种基于扩散模型、Transformer 模块和 RCNN 的无锚点实例分割模型，能有效识别肾脏活检中的肾小球、肾小管和动脉，成为新的最先进方法，达到了 51.7% 的检测准确率。