Accurate segmentation algorithm of acoustic neuroma in the cerebellopontine angle based on ACP-TransUNet

Acoustic neuroma is one of the most common tumors in the cerebellopontine angle area. Patients with acoustic neuroma have clinical manifestations of the cerebellopontine angle occupying syndrome, such as tinnitus, hearing impairment and even hearing loss. Acoustic neuromas often grow in the internal auditory canal. Neurosurgeons need to observe the lesion contour with the help of MRI images, which not only takes a lot of time, but also is easily affected by subjective factors. Therefore, the automatic and accurate segmentation of acoustic neuroma in cerebellopontine angle on MRI is of great significance for surgical treatment and expected rehabilitation. In this paper, an automatic segmentation method based on Transformer is proposed, using TransUNet as the core model. As some acoustic neuromas are irregular in shape and grow into the internal auditory canal, larger receptive fields are thus needed to synthesize the features. Therefore, we added Atrous Spatial Pyramid Pooling to CNN, which can obtain a larger receptive field without losing too much resolution. Since acoustic neuromas often occur in the cerebellopontine angle area with relatively fixed position, we combined channel attention with pixel attention in the up-sampling stage so as to make our model automatically learn different weights by adding the attention mechanism. In addition, we collected 300 MRI sequence nuclear resonance images of patients with acoustic neuromas in Tianjin Huanhu hospital for training and verification. The ablation experimental results show that the proposed method is reasonable and effective. The comparative experimental results show that the Dice and Hausdorff 95 metrics of the proposed method reach 95.74% and 1.9476 mm respectively, indicating that it is not only superior to the classical models such as UNet, PANet, PSPNet, UNet++, and DeepLabv3, but also show better performance than the newly-proposed SOTA (state-of-the-art) models such as CCNet, MANet, BiseNetv2, Swin-Unet, MedT, TransUNet, and UCTransNet

Medical Image Segmentation Review: The success of U-Net

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model achieved tremendous attention from academic and industrial researchers. Several extensions of this network have been proposed to address the scale and complexity created by medical tasks. Addressing the deficiency of the naive U-Net model is the foremost step for vendors to utilize the proper U-Net variant model for their business. Having a compendium of different variants in one place makes it easier for builders to identify the relevant research. Also, for ML researchers it will help them understand the challenges of the biological tasks that challenge the model. To address this, we discuss the practical aspects of the U-Net model and suggest a taxonomy to categorize each network variant. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. We provide a comprehensive implementation library with trained models for future research. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation. All information is gathered in https://github.com/NITR098/Awesome-U-Net repository.Comment: Submitted to the IEEE Transactions on Pattern Analysis and Machine Intelligence Journa

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

MAU-Net: Mixed attention U-Net for MRI brain tumor segmentation

Computer-aided brain tumor segmentation using magnetic resonance imaging (MRI) is of great significance for the clinical diagnosis and treatment of patients. Recently, U-Net has received widespread attention as a milestone in automatic brain tumor segmentation. Following its merits and motivated by the success of the attention mechanism, this work proposed a novel mixed attention U-Net model, i.e., MAU-Net, which integrated the spatial-channel attention and self-attention into a single U-Net architecture for MRI brain tumor segmentation. Specifically, MAU-Net embeds Shuffle Attention using spatial-channel attention after each convolutional block in the encoder stage to enhance local details of brain tumor images. Meanwhile, considering the superior capability of self-attention in modeling long-distance dependencies, an enhanced Transformer module is introduced at the bottleneck to improve the interactive learning ability of global information of brain tumor images. MAU-Net achieves enhancing tumor, whole tumor and tumor core segmentation Dice values of 77.88/77.47, 90.15/90.00 and 81.09/81.63% on the brain tumor segmentation (BraTS) 2019/2020 validation datasets, and it outperforms the baseline by 1.15 and 0.93% on average, respectively. Besides, MAU-Net also demonstrates good competitiveness compared with representative methods

GL-Segnet: Global-Local representation learning net for medical image segmentation

Medical image segmentation has long been a compelling and fundamental problem in the realm of neuroscience. This is an extremely challenging task due to the intensely interfering irrelevant background information to segment the target. State-of-the-art methods fail to consider simultaneously addressing both long-range and short-range dependencies, and commonly emphasize the semantic information characterization capability while ignoring the geometric detail information implied in the shallow feature maps resulting in the dropping of crucial features. To tackle the above problem, we propose a Global-Local representation learning net for medical image segmentation, namely GL-Segnet. In the Feature encoder, we utilize the Multi-Scale Convolution (MSC) and Multi-Scale Pooling (MSP) modules to encode the global semantic representation information at the shallow level of the network, and multi-scale feature fusion operations are applied to enrich local geometric detail information in a cross-level manner. Beyond that, we adopt a global semantic feature extraction module to perform filtering of irrelevant background information. In Attention-enhancing Decoder, we use the Attention-based feature decoding module to refine the multi-scale fused feature information, which provides effective cues for attention decoding. We exploit the structural similarity between images and the edge gradient information to propose a hybrid loss to improve the segmentation accuracy of the model. Extensive experiments on medical image segmentation from Glas, ISIC, Brain Tumors and SIIM-ACR demonstrated that our GL-Segnet is superior to existing state-of-art methods in subjective visual performance and objective evaluation

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure