Official implementation of instance loss functions, a family of instance-level segmentation and detection loss functions, to improve semantic segmentation and detection of multiple instances in biomedical images. Please cite our publications below if our proposed loss functions are beneficial to your research and work.
@article{RACHMADI2024108414,
title = {A new family of instance-level loss functions for improving instance-level segmentation and detection of white matter hyperintensities in routine clinical brain MRI},
journal = {Computers in Biology and Medicine},
pages = {108414},
year = {2024},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2024.108414},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524004980},
author = {Muhammad Febrian Rachmadi and Michal Byra and Henrik Skibbe},
}@inproceedings{rachmadi2024improving,
title={Improving segmentation of objects with varying sizes in biomedical images using instance-wise and center-of-instance segmentation loss function},
author={Rachmadi, Febrian and Poon, Charissa and Skibbe, Henrik},
booktitle={Medical Imaging with Deep Learning},
pages={286--300},
year={2024},
organization={PMLR}
}In this study, we introduce instance loss functions, a family of instance-level segmentation and detection loss functions, aimed at enhancing the training of neural networks for segmenting and detecting objects in biomedical image data, especially when objects appear in varied numbers and sizes. These functions, namely the instance segmentation loss ($\mathcal{L}{\textrm{instance}}$), the instance center loss ($\mathcal{L}{\textrm{center}}$), the false instance rate loss ($\mathcal{L}{\textrm{false}}$), and the instance proximity loss ($\mathcal{L}{\textrm{proximity}}$), focus on object instances rather than pixel-by-pixel comparisons. The $\mathcal{L}{\textrm{instance}}$ is crafted to improve instance-wise segmentation quality, $\mathcal{L}{\textrm{center}}$ is crafted to improve segmentation quality of small instances, $\mathcal{L}{\textrm{false}}$ is optimized to minimize the rate of false and missed detections across an image with a diverse range of instance sizes, and $\mathcal{L}{\textrm{proximity}}$ is crafted to improve detection by pulling predictions towards the ground truth. We benchmarked our proposed instance loss functions, both individually and in combination through ensemble inference models approach, against traditional methods using the task of segmenting white matter hyperintensities (WMH) in brain MRI. This was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the WMH Segmentation Challenge datasets, both of which exhibit a significant variation in WMH instance sizes. Our empirical evaluations revealed that combining two instance-level loss functions of through ensemble inference models approach noticeably outperformed models using other loss function on both ADNI and WMH Segmentation Challenge dataset for segmentation and detection of WMH instances.
labels) instances
1.b Instance segmentation loss for predicted segmentation (outputs) instanceslabels) instances
1.b Instance segmentation loss for predicted segmentation (outputs) instancesPlease see example_colab.ipynb, which was written specifically for Google Colaboratory. Also, folder example_blobs contains example blobs that are used for visualization in the paper.
loss_function_pixel: Any segmentation loss used to calculate the quality of segmentation in pixel-wise level. Written in the original paper as $L_{global}$ in the formalism.loss_function_instance: Any segmentation loss used to calculate the quality of segmentation in instance-wise level. Written in the original paper as $L_{instance}$ in the formalism.loss_function_center: Any segmentation loss used to calculate the center-of-instance segmentation loss. Written in the original paper as $L_{center}$ in the formalism.activation: Set the non-linear function used for segmentation in the last layer of the model. The valid inputs are "sigmoid", "softmax", or "none". For a two-class segmentation problem (i.e. background and foreground classes), either "sigmoid" or "softmax" non-linear functions can be called within the function loss. If there are more than two classes, only "sigmoid" non-linear function can be called and each channel must be calculated separately. Furthermore, user can always call any non-linear functions outside the function loss and pass both outputs and labels tensors as probability values (set activation = "none"). Default: "sigmoid"num_out_chn: Number of channels/classes that outputs and labels tensors (BNHW[D] where N is number of classes). Default: 1object_chn: Channel of outputs and labels tensors that this loss function will calculate. Note that each channel must be calculated separately. Default: 1spatial_dims: 3 for 3D data (BNHWD) or 2 for 2D data (BNHW). Default: 3reduce_segmentation: Set as "mean" if we want to calculate the average of all instance-wise segmentation losses losses or "sum" if we want to calculate the sum of all instance-wise segmentation losses. Default: "mean"instance_wise_reduce: Reducing the instance-wise segmentation losses for all instances ("instance") or batches ("data"). Default: "instance"num_iterations: Number of iterations of max-pooling to perform connected component analysis (CCA). Bigger instances need more iterations (or 1 big instance might be devided into several instances). Bigger image also tend to use more iterations as well. More iterations will use more computational time. Default: 350segmentation_threshold: Segmentation threshold to produce binary predicted segmentation before runnning the CCA. Default: 0.5max_cc_out: Maximum numbers of connected components in the outputs tensor. This is useful to cut down the computation time and memory usage in the GPUs. This is extremely useful in the early epochs where there are a lot of false predicted segmentation instances. We found that max_cc_out = 50 produces good performances and time/memory usage. Default: 50mul_too_many: Similar to the 'max_cc_out'. We found that mul_too_many = 50 produces good performances and time/memory usage. Default: 50min_instance_size: We can ignore instances that are too small. Set as 0 as the default (i.e. not in use).centroid_offset: Offset value to increase the size of center-of-mass for each instance. For example, centroid_offset = 1 will increase the size of center-of-mass of instance in 2D from 1 x 1 into 3 x 3. Default: 3 (i.e. center-of-mass's size is either 7 x 7 in 2D or 7 x 7 x 7 in 3D).smoother: Used to avoid division by 0 (numerical purposes). Default: 1e-07instance_wise_loss_no_tp: If True, the loss function does not include true positive intersections with other instances from the ground truth image (please see Appendix B of the original paper). Default: True (mainly due to successfully improving the performance in DSC). Default: Truerate_instead_number: The loss function will automatically provides the numbers of both missed and false instances. If False, the loss function will provide the exact numbers of missed and false instances (e.g. 1 missed and 6 false). If True, the loss function will provide the rate of missed and false instances (e.g. 1 / 7 = 0.1429 for missed instances and 6 / 14 = 0.4286 for false instances). Default: FalseThe minimum requirements are as follow.