Topology Aware Fully Convolutional Networks for Histology Gland Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077

pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long‐term follow‐up

Elston, C. W., & Ellis, I. O. (1991) · Histopathology · doi:10.1111/j.1365-2559.1991.tb00229.x · Cited by 6,181

Learning Deconvolution Network for Semantic Segmentation

Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213

Conditional Random Fields as Recurrent Neural Networks

Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386

Gleason grading and prognostic factors in carcinoma of the prostate

Humphrey, P. A. (2004) · Modern Pathology · doi:10.1038/modpathol.3800054 · Cited by 525

Large-Scale Object Classification Using Label Relation Graphs

Deng, J., Ding, N., Jia, Y., Frome, A., Murphy, K. P., Samy Bengio, A., Li, Y., Neven, H., & Adam, H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_4 · Cited by 371

A Stochastic Polygons Model for Glandular Structures in Colon Histology Images

Sirinukunwattana, K., Snead, D. R. J., & Rajpoot, N. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2433900 · Cited by 257

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332

Deep learning-enabled medical computer vision

Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406

Deep semantic segmentation of natural and medical images: a review

Taghanaki, S. A., Abhishek, K., Cohen, J., Cohen‐Adad, J., & Hamarneh, G. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09854-1 · Cited by 929

Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation

Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2743464 · Cited by 751

Deep neural network models for computational histopathology: A survey

Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749

Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks

Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K. S., Davidson, B. R., Pereira, S. P., Clarkson, M. J., & Barratt, D. C. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2806309 · Cited by 701

A review on deep learning in medical image analysis

Suganyadevi, S., Seethalakshmi, V., & Balasamy, K. (2021) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-021-00218-1 · Cited by 717

Combo loss: Handling input and output imbalance in multi-organ segmentation

Taghanaki, S. A., Zheng, Y., Zhou, S. K., Georgescu, B., Sharma, P., Xu, D., Comaniciu, D., & Hamarneh, G. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.04.005 · Cited by 500

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Salvi, M., Acharya, U. R., Molinari, F., & Meiburger, K. M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104129 · Cited by 420

An application of cascaded 3D fully convolutional networks for medical image segmentation

Roth, H. R., Oda, H., Zhou, X., Shimizu, N., Yang, Y., Hayashi, Y., Oda, M., Fujiwara, M., Misawa, K., & Mori, K. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.03.001 · Cited by 330

A survey on incorporating domain knowledge into deep learning for medical image analysis

Xie, X., Niu, J., Liu, X., Chen, Z., Tang, S., & Yu, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101985 · Cited by 331

RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images

Zeng, Z., Xie, W., Zhang, Y., & Lu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2896920 · Cited by 284

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

Valanarasu, J. M. J., Sindagi, V. A., Hacihaliloglu, I., & Patel, V. M. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3130469 · Cited by 295

Regularization for Deep Learning: A TaxonomyPreprint

Kukačka, J., Golkov, V., & Cremers, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.10686 · Cited by 254

EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection

Ghafoorian, M., Nugteren, C., Baka, N., Booij, O., & Hofmann, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_15 · Cited by 234

Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images

Vakanski, A., Xian, M., & Freer, P. E. (2020) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2020.06.015 · Cited by 198

RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification

Wang, S., Zhu, Y., Yu, L., Chen, H., Lin, H., Wan, X., Fan, X., & Heng, P. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101549 · Cited by 179

Deep learning in digital pathology image analysis: a survey

Deng, S., Zhang, X., Wen, Y., Chang, E., Fan, Y., Lai, M., & Xu, Y. L. (2020) · Frontiers of Medicine · doi:10.1007/s11684-020-0782-9 · Cited by 166

Learning and Incorporating Shape Models for Semantic Segmentation

Ravishankar, H., Venkataramani, R., Thiruvenkadam, S. R., Sudhakar, P., & Vaidya, V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_24 · Cited by 155

Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential

Gadermayr, M., & Tschuchnig, M. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102337 · Cited by 177

Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation

Mirikharaji, Z., & Hamarneh, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_84 · Cited by 140

Fully automatic cervical vertebrae segmentation framework for X-ray images

Arif, S. M. M. R. A., KNAPP, K. M., & Slabaugh, G. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.006 · Cited by 116

Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

Tschuchnig, M., Oostingh, G. J., & Gadermayr, M. (2020) · Patterns · doi:10.1016/j.patter.2020.100089 · Cited by 106

High-level prior-based loss functions for medical image segmentation: A survey

Jurdi, R. E., Petitjean, C., Honeiné, P., Cheplygina, V., & Abdallah, F. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103248 · Cited by 118

Structured layer surface segmentation for retina OCT using fully convolutional regression networks

He, Y., Carass, A., Liu, Y., Jedynak, B. M., Solomon, S. D., Saidha, S., Calabresi, P. A., & Prince, J. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101856 · Cited by 104

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Peng, J., & Wang, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3062380 · Cited by 115

Artificial intelligence for microscopy: what you should know

von Chamier, L., Laine, R. F., & Henriques, R. (2019) · Biochemical Society Transactions · doi:10.1042/bst20180391 · Cited by 99

Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology

Gadermayr, M., Gupta, L., Appel, V., Boor, P., Klinkhammer, B. M., & Merhof, D. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2899364 · Cited by 98

Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer

Barmpoutis, P., Capite, M. D., Kayhanian, H., Waddingham, W., Alexander, D. C., Jansen, M., & Kwong, F. N. K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256907 · Cited by 93

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Thakur, N., Yoon, H., & Chong, Y. (2020) · Cancers · doi:10.3390/cancers12071884 · Cited by 91

Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation

Bi, L., Feng, D. D., & Kim, J. (2018) · The Visual Computer · doi:10.1007/s00371-018-1519-5 · Cited by 85

Analysis of Histopathology Images

Jiménez–del–Toro, O. A., Otálora, S., Andersson, M., Eurén, K., Hedlund, M., Rousson, M., Müller, H., & Atzori, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812133-7.00010-7 · Cited by 86

Automatic Liver Lesion Detection using Cascaded Deep Residual NetworksPreprint

Bi, L., Kim, J., Kumar, A., & Feng, D. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.02703 · Cited by 96

Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders

Larrazabal, A. J., Martínez, C. E., Glocker, B., & Ferrante, E. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3005297 · Cited by 79

CNN cascades for segmenting sparse objects in gigapixel whole slide images

Gadermayr, M., Dombrowski, A.-K., Klinkhammer, B. M., Boor, P., & Merhof, D. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.11.002 · Cited by 101

BB-UNet: U-Net With Bounding Box Prior

Jurdi, R. E., Petitjean, C., Honeiné, P., & Abdallah, F. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3001502 · Cited by 67

Topological deep learning: a review of an emerging paradigm

Zia, A., Khamis, A., Nichols, J. D., Tayab, U. B., Hayder, Z., Rolland, V., Stone, E. A., & Petersson, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10710-9 · Cited by 74

Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT.

He, Y.-F., Carass, A., Liu, Y.-H., Jedynak, B., Solomon, S. D., Saidha, S., Calabresi, P., & Prince, J. L. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.005042 · Cited by 65

CNN cascades for segmenting sparse objects in gigapixel whole slide images

Gadermayr, M., Dombrowski, A.-K., Klinkhammer, B. M., Boor, P., & Merhof, D. (2019) · RWTH Publications (RWTH Aachen) · Cited by 62

Accurate Gastric Cancer Segmentation in Digital Pathology Images Using Deformable Convolution and Multi-Scale Embedding Networks

Sun, M., Zhang, G., Dang, H., Qi, X., Zhou, X., & Chang, Q. H. (2019) · IEEE Access · doi:10.1109/access.2019.2918800 · Cited by 61

clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

Shit, S., Paetzold, J. C., Sekuboyina, A., Ezhov, I., Unger, A., Zhylka, A., Pluim, J. P. W., Bauer, U., & Menze, B. H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01629 · Cited by 60

Structure Prediction for Gland Segmentation With Hand-Crafted and Deep Convolutional Features

Manivannan, S., Li, W., Zhang, J., Trucco, E., & McKenna, S. J. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2750210 · Cited by 54

Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation

Bi, L., Kim, J., Kumar, A., FULHAM, M. J., & Feng, D. D. (2017) · The Visual Computer · doi:10.1007/s00371-017-1379-4 · Cited by 51

Statistical Karyotype Analysis Using CNN and Geometric Optimization

Xie, N., Li, X., Li, K., Yang, Y., & Shen, H. T. (2019) · IEEE Access · doi:10.1109/access.2019.2951723 · Cited by 51

A Persistent Homology-Based Topological Loss for CNN-Based Multiclass Segmentation of CMR

Byrne, N., Clough, J. R., Valverde, I., Montana, G., & King, A. P. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3203309 · Cited by 53

A Deep Learning Design for Improving Topology Coherence in Blood Vessel Segmentation

Araújo, R. J., Cardoso, J. S., & Oliveira, H. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_11 · Cited by 50

Data Augmentation in Training Deep Learning Models for Medical Image Analysis

Abdollahi, B., Tomita, N., & Hassanpour, S. (2020) · Intelligent systems reference library · doi:10.1007/978-3-030-42750-4_6 · Cited by 50

Towards Topological Correct Segmentation of Macular OCT from Cascaded FCNs

He, Y., Carass, A., Yun, Y., Zhao, C., Jedynak, B., Solomon, S. D., Saidha, S., Calabresi, P. A., & Prince, J. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67561-9_23 · Cited by 48

Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders

Larrazabal, A. J., Martínez, C. E., & Ferrante, E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_65 · Cited by 47

Shape-Aware Deep Convolutional Neural Network for Vertebrae Segmentation

Arif, S. M. M. R. A., KNAPP, K. M., & Slabaugh, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-74113-0_2 · Cited by 46

Learning Topological Interactions for Multi-Class Medical Image Segmentation

Gupta, S., Hu, X., Kaan, J. H., Jin, M. X., Mpoy, M., Chung, K., Singh, G., Saltz, M. M., Kurç, T., Saltz, J. H., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19818-2_40 · Cited by 44

Which Way Round? A Study on the Performance of Stain-Translation for Segmenting Arbitrarily Dyed Histological Images

Gadermayr, M., Appel, V., Klinkhammer, B. M., Boor, P., & Merhof, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_19 · Cited by 41

Intra- and Inter-Pair Consistency for Semi-Supervised Gland Segmentation

Xie, Y., Zhang, J., Liao, Z., Verjans, J. W. H., Shen, C., & Xia, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3136716 · Cited by 41

Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology

Sobhani, F., Robinson, R., Hamidinekoo, A., Roxanis, I., Somaiah, N., & Yuan, Y. (2021) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2021.188520 · Cited by 41

Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images

Aatresh, A. A., Yatgiri, R. P., Chanchal, A. K., Kumar, A., Ravi, A., Das, D., S., R. B., Lal, S., & Kini, J. R. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101975 · Cited by 40

Removing segmentation inconsistencies with semi-supervised non-adjacency constraint

Ganaye, P.-A., Sdika, M., Triggs, B., & Benoit-Cattin, H. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101551 · Cited by 37

Deep learning segmentation for optical coherence tomography measurements of the lower tear meniscus

Stegmann, H., Werkmeister, R. M., Pfister, M., Garhöfer, G., Schmetterer, L. F., & dos Santos, V. A. (2020) · Biomedical Optics Express · doi:10.1364/boe.386228 · Cited by 36

Neural multi-atlas label fusion: Application to cardiac MR images

Yang, H., Sun, J. Z., Li, H., Wang, L., & Xu, Z. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.07.009 · Cited by 35

A Deep Level Set Method for Image Segmentation

Tang, M., Valipour, S., Zhang, Z., Cobzaş, D., & Jägersand, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9_15 · Cited by 34

Domain‐specific data augmentation for segmenting MR images of fatty infiltrated human thighs with neural networks

Gadermayr, M., Li, K., Müller, M., Truhn, D., Kraemer, N. A., Merhof, D., & Gess, B. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26544 · Cited by 31

LF-UNet – A novel anatomical-aware dual-branch cascaded deep neural network for segmentation of retinal layers and fluid from optical coherence tomography images

Ma, D., Lu, D., Chen, S., Heisler, M., Dabiri, S., Lee, S., Lee, H., Ding, G. W., Šarunic, M. V., & Beg, M. F. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101988 · Cited by 31

Prior-aware autoencoders for lung pathology segmentation

Astaraki, M., Smedby, Ö., & Wang, C. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102491 · Cited by 31

A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI

Byrne, N., Clough, J. R., Montana, G., & King, A. P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68107-4_1 · Cited by 29

The semiotics of medical image Segmentation

Baxter, J. S. H., Gibson, E., Eagleson, R. A., & Peters, T. M. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.11.007 · Cited by 28

Pairwise learning for medical image segmentation

Wang, R., Cao, S., Ma, K., Zheng, Y., & Meng, D. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101876 · Cited by 27

Segmentation and classification of knee joint ultrasonic image via deep learning

Long, Z., Zhang, X., Li, C., Niu, J., Wu, X., & Li, Z. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106765 · Cited by 26

Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images

Yan, R., Ren, F., Li, J., Rao, X., Lv, Z., Zheng, C.-H., & Zhang, F. (2022) · Sensors · doi:10.3390/s22114061 · Cited by 24

Towards Automatic 3D Shape Instantiation for Deployed Stent Grafts: 2D Multiple-class and Class-imbalance Marker Segmentation with Equally-weighted Focal U-Net

Zhou, X.-Y., Riga, C., Su-Lin, L., & Yang, G. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594178 · Cited by 24

Joint Deep Learning of Foreground, Background and Shape for Robust Contextual Segmentation

Ravishankar, H., Thiruvenkadam, S., Venkataramani, R., & Vaidya, V. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_49 · Cited by 23

Hierarchical Multi-class Segmentation of Glioma Images Using Networks with Multi-level Activation Function

Hu, X., Li, H., Zhao, Y. S., Dong, C., Menze, B. H., & Piraud, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11726-9_11 · Cited by 24

Uncertainty Driven Multi-loss Fully Convolutional Networks for Histopathology

BenTaieb, A., & Hamarneh, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67534-3_17 · Cited by 22

Weakly-Supervised 3D Medical Image Segmentation Using Geometric Prior and Contrastive Similarity

Du, H., Dong, Q., Xu, Y. L., & Liao, J. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3269523 · Cited by 22

Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration

Bieth, M., Peter, L., Nekolla, S. G., Eiber, M. J., Langs, G., Schwaiger, M., & Menze, B. H. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2720261 · Cited by 20

Neuron Segmentation Using Deep Complete Bipartite Networks

Chen, J., Banerjee, S., Grama, A., Scheirer, W. J., & Chen, D. Z. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_3 · Cited by 20

State of machine and deep learning in histopathological applications in digestive diseases

Kobayashi, S., Saltz, J. H., & Yang, V. W. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i20.2545 · Cited by 20

Unbiased Shape Compactness for Segmentation

Dolz, J., Ayed, I. B., & Desrosiers, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_86 · Cited by 18

A dense multi-path decoder for tissue segmentation in histopathology images

Vu, Q. D., & Kwak, J. T. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.03.007 · Cited by 18

Edge-aware Fully Convolutional Network with CRF-RNN Layer for Hippocampus Segmentation

Wang, S., Yi, L., Chen, Q., Meng, Z., Dong, H., & He, Z. W. (2019) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic.2019.8785801 · Cited by 17

Active Learning for Patch-Based Digital Pathology Using Convolutional Neural Networks to Reduce Annotation Costs

Carse, J., & McKenna, S. J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23937-4_3 · Cited by 16

PBPK-Adapted Deep Learning for Pretherapy Prediction of Voxelwise Dosimetry: In-Silico Proof of Concept

Kassar, M., Drobnjaković, M., Birindelli, G., Xue, S., Gafita, A., Wendler, T., Afshar‐Oromieh, A., Navab, N., Weber, W., Eiber, M., et al. (2024) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2024.3381849 · Cited by 16

Enhancing Surgical Guidance: Deep Learning-Based Liver Vessel Segmentation in Real-Time Ultrasound Video Frames

Awais, M., Taie, M. A., O’Connor, C. S., Castelo, A. H., Acidi, B., Cao, H. S. T., & Brock, K. K. (2024) · Cancers · doi:10.3390/cancers16213674 · Cited by 14

Incorporating prior shape knowledge via data-driven loss model to improve 3D liver segmentation in deep CNNs

Mohagheghi, S., & Foruzan, A. H. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02085-y · Cited by 14

Structural Priors Guided Network for the Corneal Endothelial Cell Segmentation

Zhang, Y., Xi, R., Zeng, L., Towey, D., Bai, R., Higashita, R., & Liu, J. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3300656 · Cited by 13

NANet: Nuclei-Aware Network for Grading of Breast Cancer in HE Stained Pathological Images

Yan, R., Li, J., Rao, X., Lv, Z., Zheng, C.-H., Dou, J., Wang, X., Ren, F., & Zhang, F. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313329 · Cited by 13

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

Huss, R., Raffler, J., & Märkl, B. (2023) · Cancer Reports · doi:10.1002/cnr2.1796 · Cited by 13

Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide ImagesPreprint

Tian, Y., Li, Y., Wang, W., Zhang, J., Tang, Q., Ji, M., Yu, Y., Li, Y., Yang, H. W., & Qian, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10959 · Cited by 17

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images

Zhang, S., Yuan, Z., Zhou, X., Wang, H., Chen, B., & Wang, Y. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108178 · Cited by 12

Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation

Yue, X., Huang, X., Xu, Z., Chen, Y., & Xu, C. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103189 · Cited by 12

Discovery and generalization of tissue structures from spatial omics data

Wu, Z., Kondo, A., McGrady, M., Baker, E. A. G., Chidester, B., Wu, E., Rahim, M. K., Bracey, N. A., Charu, V., Cho, R. J., et al. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100838 · Cited by 13

Generalising multistain immunohistochemistry tissue segmentation using end‐to‐end colour deconvolution deep neural networks

Lahiani, A., Gildenblat, J., Klaman, I., Navab, N., & Klaiman, E. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.6513 · Cited by 22

Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network

Wang, R., Cao, S., Ma, K., Meng, D., & Zheng, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_18 · Cited by 12

A Fully Convolutional Network for Signature Segmentation from Document Images

Melo, V. K. S. L., & Bezerra, B. L. D. (2018) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr-2018.2018.00100 · Cited by 11

A Distance-Based Loss for Smooth and Continuous Skin Layer Segmentation in Optoacoustic Images

Gerl, S., Paetzold, J. C., He, H., Ezhov, I., Shit, S., Kofler, F., Bayat, A., Tetteh, G., Ntziachristos, V., & Menze, B. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59725-2_30 · Cited by 11

Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour DetectionPreprint

Zhang, Z., Xing, F., Su, H., Shi, X., & Yang, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.07281 · Cited by 14

Topologically Faithful Multi-class Segmentation in Medical Images

Berger, A. H., Lux, L., Stucki, N., Bürgin, V., Shit, S., Banaszak, A., Rueckert, D., Bauer, U., & Paetzold, J. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72111-3_68 · Cited by 10

Artificial intelligence for pathology

Xing, F., Zhang, X., & Cornish, T. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00011-9 · Cited by 13

Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

Tschuchnig, M., Oostingh, G. J., & Gadermayr, M. (2020) · Patterns · doi:10.1016/j.patter.2020.100144 · Cited by 10

A Line to Align: Deep Dynamic Time Warping for Retinal OCT Segmentation

Maier, H., Faghihroohi, S., & Navab, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87193-2_67 · Cited by 10

Segmentation of gastric cancer from microscopic biopsy images using deep learning approach

Rasal, T., Veerakumar, T., Subudhi, B. N., & Esakkirajan, S. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105250 · Cited by 9

I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation

Samson, L., van Noord, N., Booij, O., Hofmann, M., Gavves, E., & Ghafoorian, M. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00124 · Cited by 9

TA-Net: Topology-Aware Network for Gland Segmentation

Wang, H., Xian, M., & Vakanski, A. (2022) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00330 · Cited by 28

Introducing soft topology constraints in deep learning-based segmentation using projected pooling loss

Fu, G., Jurdi, R. E., Chougar, L., Dormont, D., Valabrègue, R., Lehéricy, S., & Colliot, O. (2023) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2651576 · Cited by 3

Deep learning meets topology-preserving active contours: towards scalable quantitative histology of cortical cytoarchitecture.Preprint

Thierbach, K., Bazin, P., Gavriilidis, F., Kirilina, E. P., Jager, C., Morawski, M., Geyer, S., Weiskopf, N., & Scherf, N. (2019) · Data Archiving and Networked Services (DANS) · Cited by 2

A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology

Clough, J. R., Byrne, N., Öksüz, İ., Zimmer, V. A., Schnabel, J. A., & King, A. P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3013679 · Cited by 281

Topology-Aware Segmentation Using Discrete Morse Theory

Hu, X., Wang, Y., Li, F., Samaras, D., & Chen, C. (2021) · PubMed · doi:10.48550/arxiv.2103.09992 · Cited by 38