AdaptIS: Adaptive Instance Selection Network

Sofiiuk, Konstantin; Barinova, Olga; Konushin, Anton

doi:10.1109/iccv.2019.00745

Cited by 182 publications

(102 citation statements)

References 27 publications

(50 reference statements)

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“…It is in fact very difficult to achieve cell instance segmentation using weakly supervised learning-based methods, so there are not many methods. However, methods based on weakly supervised learning (Brabandere et al, 2017;Sofiiuk et al, 2019;Wang et al, 2020) in the domain of natural image processing can be used for reference in the domain of medical image processing to achieve cell instance segmentation.…”

Section: Weakly Supervised Learning-based Methodsmentioning

confidence: 99%

Macaque Neuron Instance Segmentation only with Point Annotations based on Multiscale Fully Convolutional Regression Neural Network

You

Jiang

Shi

et al. 2021

Preprint

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In the field of biomedicine, instance segmentation / individualization is important in analyzing the number, the morphology and the distribution of neurons for the whole slide images. Traditionally, biologists apply stereology technique to manually count the number of neurons in the regions of interest and estimate the number in anatomical regions or the entire brain. This is very tedious and time-consuming. In this paper, we propose a multiscale fully convolutional regression neural network combined with competitive region growing technique to individualize size-varying and touching neurons in major anatomical regions of macaque brain. Given that neuron instance or contour annotations are infeasible to obtain in certain regions, such as dentate gyrus where thousands of touching neurons are present, we ask expert to perform point annotations in the center location of neurons (noted as neuron centroids) for training. Thanks to the multiscale resolution achieved by parallel multiple receptive fields and different network depths, our proposed network succeeds in detecting the centroids of size-varying and touching neurons. Competitive region growing is then applied on these centroids to achieve neuron instance segmentation. Experiments on macaque brain data suggest that our proposed method outperform the state-of-the-art methods in terms of neuron instance segmentation performance. To our knowledge, this is the first deep learning research work to individualize size-varying and touching neurons only using point annotations in major anatomical regions in the macaque brain. The cartographic representation of the number and the average orientation of neurons illustrates the synthesis of multi-gigabyte image information in the form of cartography, and shows the characteristics of different anatomical regions from mesoscopic level.

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Section: Weakly Supervised Learning-based Methodsmentioning

confidence: 99%

Macaque Neuron Instance Segmentation only with Point Annotations based on Multiscale Fully Convolutional Regression Neural Network

You

Jiang

Shi

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Subsequently, UPSNet (Xiong et al 2019) introduces a parameter-free panoptic head to address the problem of overlapping of instances and also predicts an extra unknown class. More recently, AdaptIS (Sofiiuk et al 2019) uses point proposals to produce instance masks and jointly trains with a standard semantic segmentation pipeline to perform panoptic segmentation. In contrast, Porzi et al (2019) propose an architecture for panoptic segmentation that effectively integrates contextual information from a lightweight DeepLab-inspired module with multi-scale features from a FPN.…”

Section: Related Workmentioning

confidence: 99%

EfficientPS: Efficient Panoptic Segmentation

2021

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Understanding the scene in which an autonomous robot operates is critical for its competent functioning. Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task. In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head. We also propose a novel panoptic fusion module that congruously integrates the output logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark. Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.

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“…In this case, the generator net consists of a convolutional net that, given a point in the image, finds the segment containing that point (Figure 7). 38,30,39 Picking different points in the image will lead the net to predict different segments. This can occur even if the point is in the same segment.…”

Section: Hierarchical Instance Segmentation Using a Unified Ges Netmentioning

confidence: 99%

Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

Eppel

Xu²,

Bismuth³

et al. 2020

Preprint

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This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in the chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder, …). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. @ The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.

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AdaptIS: Adaptive Instance Selection Network

Cited by 182 publications

References 27 publications

Macaque Neuron Instance Segmentation only with Point Annotations based on Multiscale Fully Convolutional Regression Neural Network

Macaque Neuron Instance Segmentation only with Point Annotations based on Multiscale Fully Convolutional Regression Neural Network

EfficientPS: Efficient Panoptic Segmentation

Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

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