With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.

In laparoscopic surgery, image quality can be severely degraded by surgical smoke, which not only introduces errors for the image processing algorithms (used in image guided surgery), but also reduces the visibility of the observed organs and tissues. To overcome these drawbacks, this work aims to remove smoke in laparoscopic images using an image preprocessing method based on a variational approach. In this paper, we present the physical smoke model where the degraded image is separated into two parts: direct attenuation and smoke veil and propose an efficient variational-based desmoking method for laparoscopic images. To estimate the smoke veil, the proposed method relies on the observation that smoke veil has low contrast and low inter-channel differences. A cost function is defined based on this prior knowledge and is solved using an augmented Lagrangian method. The obtained smoke veil is then subtracted from the original degraded image, resulting in the direct attenuation part. Finally, the smoke free image is computed using a linear intensity transformation of the direct attenuation part. The performance of the proposed method is evaluated quantitatively and qualitatively using three datasets: two public real smoked laparoscopic datasets and one generated synthetic dataset. No-reference and reduced-reference image quality assessment metrics are used with the two real datasets, and show that the proposed method outperforms the state-of-the-art ones. Besides, standard full-reference ones are employed with the synthetic dataset, and indicate also the good performance of the proposed method. Furthermore, the qualitative visual inspection of the results shows that our method removes smoke effectively from the laparoscopic images. All the obtained results show that the proposed approach reduces the smoke effectively while preserving the important perceptual information of the image. This allows to provide a better visualization of the operation field for surgeons and improve the image guided laparoscopic surgery procedure.

Deep networks have made remarkable progress in Multi-View Stereo (MVS) task in recent years. However, the problem of finding accurate correspondences across different views under ill-posed matching situations remains unresolved and crucial. To address this issue, this paper proposes a Geometry-enhanced Attentive Multi-View Stereo (GA-MVS) network, which can access multi-view consistent feature representation and achieve accurate depth estimation in challenging situations. Specifically, we propose a geometry-enhanced feature extractor to explore illumination-invariant geometric features and incorporate them with common texture features to improve matching accuracy when dealing with view-dependent photometric effects, such as shadow and specularity. Then, we design a novel attentive learning framework to explore per-pixel adaptive supervision, effectively improving the depth estimation performance of textureless regions. The experimental results on the DTU and Tanks & Temples benchmarks demonstrate that our method achieves state-of-the-art results compared to other advanced MVS models.

The mainstream techniques to detect microaneurysm in medical images involve the use of stacked convolutional neural networks. But these techniques can not provide adequate detecting performance for the MA scenarios with limited information and different sizes. In this article, a hierarchical pyramid network with a ‘T’ structure is proposed to detect microaneurysms (MAs) in retinal fundus images, which can overcome the difficulties caused by the above scenarios.We design a data preparation technique that can adaptively compute the best receptive fields of MAs to generate various patch sizes, resulting in multi-size datasets for training. The ‘T’ network consists of two pyramid feature extractors, rather than a deep network to adequately extract features of MAs and avoid feature loss caused by deep layers. Experiments on five public retinal datasets demonstrate that our method achieves the state-of-the-art performance.