Focal Dice Loss-Based V-Net for Liver Segments Classification

Prencipe, Berardino; Altini, Nicola; Cascarano, Giacomo Donato; Brunetti, Antonio; Guerriero, Andrea; Bevilacqua, Vitoantonio

doi:10.3390/app12073247

Cited by 19 publications

(6 citation statements)

References 53 publications

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“…The segmentation phase is a milestone for the detection phase; this step aims to discriminate between cell nuclei and the background. semantic segmentation architectures play a role of pivotal importance in deep learning-based medical image analysis [ 9 , 29 , 30 , 31 ]. It is a process that associates a label or a category to each pixel of an input image, thus allowing the pixelwise spatial localization of each object category appearing in the scene.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Machine learning-based nuclear segmentation methods are typically the most efficient, as they can learn to identify variations in the shape and coloration of nuclei. In the semantic segmentation [ 9 , 10 ] approach, all image pixels are labeled as nuclear or background through a deep learning model. Nevertheless, these methods often fail to distinguish the different instances of objects of interest, i.e., nuclei, which then need to be addressed with ad hoc post-processing techniques, such as clustering [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM

et al. 2022

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Nuclei identification is a fundamental task in many areas of biomedical image analysis related to computational pathology applications. Nowadays, deep learning is the primary approach by which to segment the nuclei, but accuracy is closely linked to the amount of histological ground truth data for training. In addition, it is known that most of the hematoxylin and eosin (H&E)-stained microscopy nuclei images contain complex and irregular visual characteristics. Moreover, conventional semantic segmentation architectures grounded on convolutional neural networks (CNNs) are unable to recognize distinct overlapping and clustered nuclei. To overcome these problems, we present an innovative method based on gradient-weighted class activation mapping (Grad-CAM) saliency maps for image segmentation. The proposed solution is comprised of two steps. The first is the semantic segmentation obtained by the use of a CNN; then, the detection step is based on the calculation of local maxima of the Grad-CAM analysis evaluated on the nucleus class, allowing us to determine the positions of the nuclei centroids. This approach, which we denote as NDG-CAM, has performance in line with state-of-the-art methods, especially in isolating the different nuclei instances, and can be generalized for different organs and tissues. Experimental results demonstrated a precision of 0.833, recall of 0.815 and a Dice coefficient of 0.824 on the publicly available validation set. When used in combined mode with instance segmentation architectures such as Mask R-CNN, the method manages to surpass state-of-the-art approaches, with precision of 0.838, recall of 0.934 and a Dice coefficient of 0.884. Furthermore, performance on the external, locally collected validation set, with a Dice coefficient of 0.914 for the combined model, shows the generalization capability of the implemented pipeline, which has the ability to detect nuclei not only related to tumor or normal epithelium but also to other cytotypes.

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM

et al. 2022

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“…The encoder–decoder structures have been implemented in different convolutional network architectures, including SegNet [ 30 ], U-Net [ 31 ], U-Net 3D [ 32 ], and V-Net [ 33 ]. Besides prostate segmentation, applications in medical imaging tasks of those architectures encompass liver vessels delineation [ 34 ], segments classification [ 35 ], lung COVID-19 lesions segmentation [ 36 ], and vertebrae segmentation [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

et al. 2022

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In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.

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“…To further optimize the performance of lane line detection in multi-task learning networks, a combination loss function combining Focal Loss and Dice Loss [ 31 , 32 ] is used for model training. Dice Loss and Focal Loss respectively target the influence of factors such as diverse lane line shapes, colors, and frequent obscuration, emphasize the detection of lane line pixels, and improve the model’s attention to lane lines, while optimizing the problem of a large number of background pixels in the training dataset.…”

Section: Fusion Perception Algorithmmentioning

confidence: 99%

A panoramic driving perception fusion algorithm based on multi-task learning

Wu,

Liu,

Zheng

2024

PLoS ONE

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With the rapid development of intelligent connected vehicles, there is an increasing demand for hardware facilities and onboard systems of driver assistance systems. Currently, most vehicles are constrained by the hardware resources of onboard systems, which mainly process single-task and single-sensor data. This poses a significant challenge in achieving complex panoramic driving perception technology. While the panoramic driving perception algorithm YOLOP has achieved outstanding performance in multi-task processing, it suffers from poor adaptability of feature map pooling operations and loss of details during downsampling. To address these issues, this paper proposes a panoramic driving perception fusion algorithm based on multi-task learning. The model training involves the introduction of different loss functions and a series of processing steps for lidar point cloud data. Subsequently, the perception information from lidar and vision sensors is fused to achieve synchronized processing of multi-task and multi-sensor data, thereby effectively improving the performance and reliability of the panoramic driving perception system. To evaluate the performance of the proposed algorithm in multi-task processing, the BDD100K dataset is used. The results demonstrate that, compared to the YOLOP model, the multi-task learning network performs better in lane detection, drivable area detection, and vehicle detection tasks. Specifically, the lane detection accuracy improves by 11.6%, the mean Intersection over Union (mIoU) for drivable area detection increases by 2.1%, and the mean Average Precision at 50% IoU (mAP50) for vehicle detection improves by 3.7%.

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Focal Dice Loss-Based V-Net for Liver Segments Classification

Cited by 19 publications

References 53 publications

NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM

NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

A panoramic driving perception fusion algorithm based on multi-task learning

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